{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:26:30Z","timestamp":1760171190041,"version":"3.40.3"},"publisher-location":"Cham","reference-count":105,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030527044"},{"type":"electronic","value":"9783030527051"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-52705-1_10","type":"book-chapter","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T23:19:19Z","timestamp":1594077559000},"page":"137-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review"],"prefix":"10.1007","author":[{"given":"Andrea","family":"Campagner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federico","family":"Cabitza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8083-7809","authenticated-orcid":false,"given":"Davide","family":"Ciucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"key":"10_CR1","first-page":"47","volume":"118","author":"MK Afridi","year":"2020","unstructured":"Afridi, M.K., Azam, N., Yao, J.: Variance based three-way clustering approaches for handling overlapping clustering. IJAR 118, 47\u201363 (2020)","journal-title":"IJAR"},{"key":"10_CR2","first-page":"11","volume":"98","author":"MK Afridi","year":"2018","unstructured":"Afridi, M.K., Azam, N., Yao, J., et al.: A three-way clustering approach for handling missing data using GTRS. IJAR 98, 11\u201324 (2018)","journal-title":"IJAR"},{"key":"10_CR3","unstructured":"Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487\u2013499 (1994)"},{"key":"10_CR4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neucom.2016.04.015","volume":"205","author":"M Amiri","year":"2016","unstructured":"Amiri, M., Jensen, R.: Missing data imputation using fuzzy-rough methods. Neurocomputing 205, 152\u2013164 (2016)","journal-title":"Neurocomputing"},{"key":"10_CR5","unstructured":"Awasthi, P., Blum, A., Haghtalab, N., et al.: Efficient PAC learning from the crowd. arXiv preprint arXiv:1703.07432 (2017)"},{"issue":"8","key":"10_CR6","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1108\/02635570310497657","volume":"103","author":"ML Brown","year":"2003","unstructured":"Brown, M.L., Kros, J.F.: Data mining and the impact of missing data. Ind. Manag. Data Syst. 103(8), 611\u2013621 (2003)","journal-title":"Ind. Manag. Data Syst."},{"issue":"3","key":"10_CR7","first-page":"1","volume":"45","author":"SV Buuren","year":"2010","unstructured":"Buuren, S.V., Groothuis-Oudshoorn, K.: Mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45(3), 1\u201367 (2010)","journal-title":"J. Stat. Softw."},{"key":"10_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-030-29726-8_3","volume-title":"Machine Learning and Knowledge Extraction","author":"F Cabitza","year":"2019","unstructured":"Cabitza, F., Campagner, A., Ciucci, D.: New frontiers in explainable AI: understanding the GI to interpret the GO. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 27\u201347. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29726-8_3"},{"issue":"3","key":"10_CR9","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1177\/1460458218824705","volume":"25","author":"F Cabitza","year":"2019","unstructured":"Cabitza, F., Locoro, A., Alderighi, C., et al.: The elephant in the record: on the multiplicity of data recording work. Health Inform. J. 25(3), 475\u2013490 (2019)","journal-title":"Health Inform. J."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Campagner, A., Cabitza, F., Ciucci, D.: Exploring medical data classification with three-way decision tree. In: Proceedings of BIOSTEC 2019 - Volume 5: HEALTHINF, pp. 147\u2013158. SCITEPRESS (2019)","DOI":"10.5220\/0007571001470158"},{"key":"10_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-030-22815-6_22","volume-title":"Rough Sets","author":"A Campagner","year":"2019","unstructured":"Campagner, A., Cabitza, F., Ciucci, D.: Three-way classification: ambiguity and abstention in machine learning. In: Mih\u00e1lyde\u00e1k, T., et al. (eds.) IJCRS 2019. LNCS (LNAI), vol. 11499, pp. 280\u2013294. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22815-6_22"},{"key":"10_CR12","first-page":"292","volume":"119","author":"A Campagner","year":"2020","unstructured":"Campagner, A., Cabitza, F., Ciucci, D.: The three-way-in and three-way-out framework to treat and exploit ambiguity in data. IJAR 119, 292\u2013312 (2020)","journal-title":"IJAR"},{"key":"10_CR13","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1007\/978-3-319-91476-3_61","volume-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations","author":"A Campagner","year":"2018","unstructured":"Campagner, A., Ciucci, D.: Three-way and semi-supervised decision tree learning based on orthopartitions. In: Medina, J., et al. (eds.) IPMU 2018. CCIS, vol. 854, pp. 748\u2013759. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91476-3_61"},{"key":"10_CR14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.knosys.2019.05.018","volume":"180","author":"A Campagner","year":"2019","unstructured":"Campagner, A., Ciucci, D.: Orthopartitions and soft clustering: soft mutual information measures for clustering validation. Knowl.-Based Syst. 180, 51\u201361 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Campagner, A., Ciucci, D., Svensson, C.M., et al.: Ground truthing from multi-rater labelling with three-way decisions and possibility theory. IEEE Trans. Fuzzy Syst. (2020, submitted)","DOI":"10.1016\/j.ins.2020.09.049"},{"key":"10_CR16","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2017.04.008","volume":"127","author":"Y Chen","year":"2017","unstructured":"Chen, Y., Yue, X., Fujita, H., et al.: Three-way decision support for diagnosis on focal liver lesions. Knowl.-Based Syst. 127, 85\u201399 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR17","first-page":"1501","volume":"12","author":"T Cour","year":"2011","unstructured":"Cour, T., Sapp, B., Taskar, B.: Learning from partial labels. J. Mach. Learn. Res. 12, 1501\u20131536 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Dai, D., Zhou, X., Li, H., et al.: Co-training based sequential three-way decisions for cost-sensitive classification. In: 2019 IEEE 16th ICNSC, pp. 157\u2013162 (2019)","DOI":"10.1109\/ICNSC.2019.8743205"},{"key":"10_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/978-3-319-99368-3_29","volume-title":"Rough Sets","author":"MR Depaolini","year":"2018","unstructured":"Depaolini, M.R., Ciucci, D., Calegari, S., Dominoni, M.: External indices for rough clustering. In: Nguyen, H.S., Ha, Q.-T., Li, T., Przyby\u0142a-Kasperek, M. (eds.) IJCRS 2018. LNCS (LNAI), vol. 11103, pp. 378\u2013391. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99368-3_29"},{"key":"10_CR20","unstructured":"D\u00fcntsch, I., Gediga, G.: Rough set data analysis\u2013a road to non-invasiveknowledge discovery. Methodos (2000)"},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/978-1-4757-4919-9_20","volume-title":"Decision Making: Recent Developments and Worldwide Applications","author":"S Greco","year":"2000","unstructured":"Greco, S., Matarazzo, B., Slowinski, R.: Dealing with missing data in rough set analysis of multi-attribute and multi-criteria decision problems. In: Zanakis, S.H., Doukidis, G., Zopounidis, C. (eds.) Decision Making: Recent Developments and Worldwide Applications, pp. 295\u2013316. Springer, Boston (2000). https:\/\/doi.org\/10.1007\/978-1-4757-4919-9_20"},{"key":"10_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/3-540-45554-X_46","volume-title":"Rough Sets and Current Trends in Computing","author":"JW Grzymala-Busse","year":"2001","unstructured":"Grzymala-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 378\u2013385. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-45554-X_46"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Heinecke, S., Reyzin, L.: Crowdsourced PAC learning under classification noise. In: Proceedings of AAAI HCOMP 2019, vol. 7, pp. 41\u201349 (2019)","DOI":"10.1609\/hcomp.v7i1.5279"},{"issue":"2","key":"10_CR24","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40708-016-0042-6","volume":"3","author":"A Holzinger","year":"2016","unstructured":"Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf. 3(2), 119\u2013131 (2016)","journal-title":"Brain Inf."},{"key":"10_CR25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.knosys.2016.01.036","volume":"98","author":"BQ Hu","year":"2016","unstructured":"Hu, B.Q., Wong, H., Yiu, K.F.C.: The aggregation of multiple three-way decision spaces. Knowl.-Based Syst. 98, 241\u2013249 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-3-319-99368-3_47","volume-title":"Rough Sets","author":"M Hu","year":"2018","unstructured":"Hu, M., Deng, X., Yao, Y.: A sequential three-way approach to constructing a co-association matrix in consensus clustering. In: Nguyen, H.S., Ha, Q.-T., Li, T., Przyby\u0142a-Kasperek, M. (eds.) IJCRS 2018. LNCS (LNAI), vol. 11103, pp. 599\u2013613. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99368-3_47"},{"key":"10_CR27","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2018.11.022","volume":"165","author":"M Hu","year":"2019","unstructured":"Hu, M., Yao, Y.: Structured approximations as a basis for three-way decisions in rough set theory. Knowl.-Based Syst. 165, 92\u2013109 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR28","first-page":"218","volume":"83","author":"C Huang","year":"2017","unstructured":"Huang, C., Li, J., Mei, C., et al.: Three-way concept learning based on cognitive operators: an information fusion viewpoint. IJAR 83, 218\u2013242 (2017)","journal-title":"IJAR"},{"key":"10_CR29","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-319-23525-7_16","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"E H\u00fcllermeier","year":"2015","unstructured":"H\u00fcllermeier, E., Cheng, W.: Superset learning based on generalized loss minimization. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9285, pp. 260\u2013275. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23525-7_16"},{"issue":"4","key":"10_CR30","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1145\/1634.1886","volume":"31","author":"T Imieli\u0144ski","year":"1984","unstructured":"Imieli\u0144ski, T., Lipski Jr., W.: Incomplete information in relational databases. J. ACM 31(4), 761\u2013791 (1984)","journal-title":"J. ACM"},{"key":"10_CR31","first-page":"324","volume":"113","author":"X Jia","year":"2019","unstructured":"Jia, X., Deng, Z., Min, F., Liu, D.: Three-way decisions based feature fusion for chinese irony detection. IJAR 113, 324\u2013335 (2019)","journal-title":"IJAR"},{"key":"10_CR32","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ins.2019.01.067","volume":"485","author":"X Jia","year":"2019","unstructured":"Jia, X., Li, W., Shang, L.: A multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model. Inf. Sci. 485, 248\u2013262 (2019)","journal-title":"Inf. Sci."},{"key":"10_CR33","unstructured":"Klir, G.J., Wierman, M.J.: Uncertainty-based information: elements of generalized information theory, vol. 15. Physica (2013)"},{"key":"10_CR34","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-540-25929-9_70","volume-title":"Rough Sets and Current Trends in Computing","author":"D Li","year":"2004","unstructured":"Li, D., Deogun, J., Spaulding, W., Shuart, B.: Towards missing data imputation: a study of fuzzy k-means clustering method. In: Tsumoto, S., S\u0142owi\u0144ski, R., Komorowski, J., Grzyma\u0142a-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 573\u2013579. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-25929-9_70"},{"issue":"1","key":"10_CR35","first-page":"116","volume":"55","author":"F Li","year":"2014","unstructured":"Li, F., Ye, M., Chen, X.: An extension to rough c-means clustering based on decision-theoretic rough sets model. IJAR 55(1), 116\u2013129 (2014)","journal-title":"IJAR"},{"key":"10_CR36","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.knosys.2015.07.040","volume":"91","author":"H Li","year":"2016","unstructured":"Li, H., Zhang, L., Huang, B., et al.: Sequential three-way decision and granulation for cost-sensitive face recognition. Knowl.-Based Syst. 91, 241\u2013251 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR37","first-page":"68","volume":"85","author":"H Li","year":"2017","unstructured":"Li, H., Zhang, L., Zhou, X., et al.: Cost-sensitive sequential three-way decision modeling using a deep neural network. IJAR 85, 68\u201378 (2017)","journal-title":"IJAR"},{"issue":"2","key":"10_CR38","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s13042-015-0337-6","volume":"8","author":"Y Li","year":"2017","unstructured":"Li, Y., Zhang, Z.H., Chen, W.B., et al.: TDUP: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating. IJMLC 8(2), 441\u2013453 (2017). https:\/\/doi.org\/10.1007\/s13042-015-0337-6","journal-title":"IJMLC"},{"key":"10_CR39","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.asoc.2015.01.008","volume":"29","author":"D Liang","year":"2015","unstructured":"Liang, D., Pedrycz, W., Liu, D., Hu, P.: Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making. Appl. Soft Comput. 29, 256\u2013269 (2015)","journal-title":"Appl. Soft Comput."},{"key":"10_CR40","unstructured":"Lingras, P., West, C.: Interval set clustering of web users with rough k-means. Technical report 2002-002, Department of Mathematics and Computing Science, St. Mary\u2019s University, Halifax, NS, Canada (2002)"},{"issue":"1","key":"10_CR41","first-page":"197","volume":"55","author":"D Liu","year":"2014","unstructured":"Liu, D., Li, T., Liang, D.: Incorporating logistic regression to decision-theoretic rough sets for classifications. IJAR 55(1), 197\u2013210 (2014)","journal-title":"IJAR"},{"key":"10_CR42","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.knosys.2015.07.036","volume":"91","author":"D Liu","year":"2016","unstructured":"Liu, D., Liang, D., Wang, C.: A novel three-way decision model based on incomplete information system. Knowl.-Based Syst. 91, 32\u201345 (2016). Three-way Decisions and Granular Computing","journal-title":"Knowl.-Based Syst."},{"key":"10_CR43","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ins.2019.05.010","volume":"495","author":"J Liu","year":"2019","unstructured":"Liu, J., Li, H., Zhou, X., et al.: An optimization-based formulation for three-way decisions. Inf. Sci. 495, 185\u2013214 (2019)","journal-title":"Inf. Sci."},{"key":"10_CR44","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.ins.2018.10.012","volume":"476","author":"C Luo","year":"2019","unstructured":"Luo, C., Li, T., Huang, Y., et al.: Updating three-way decisions in incomplete multi-scale information systems. Inf. Sci. 476, 274\u2013289 (2019)","journal-title":"Inf. Sci."},{"key":"10_CR45","doi-asserted-by":"crossref","first-page":"105251","DOI":"10.1016\/j.knosys.2019.105251","volume":"191","author":"J Luo","year":"2020","unstructured":"Luo, J., Fujita, H., Yao, Y., Qin, K.: On modeling similarity and three-way decision under incomplete information in rough set theory. Knowl.-Based Syst. 191, 105251 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2015.10.026","volume":"91","author":"M Ma","year":"2016","unstructured":"Ma, M.: Advances in three-way decisions and granular computing. Knowl.-Based Syst. 91, 1\u20133 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Mandel, D.R.: Counterfactual and causal explanation: from early theoretical views to new frontiers. In: The Psychology of Counterfactual Thinking, pp. 23\u201339. Routledge (2007)","DOI":"10.4324\/9780203963784"},{"key":"10_CR48","unstructured":"Miao, D., Gao, C., Zhang, N.: Three-way decisions-based semi-supervised learning. In: Theory and Applications of Three-Way Decisions, pp. 17\u201333 (2012)"},{"issue":"5","key":"10_CR49","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1007\/s00500-017-2879-x","volume":"23","author":"F Min","year":"2019","unstructured":"Min, F., Liu, F.L., Wen, L.Y., et al.: Tri-partition cost-sensitive active learning through kNN. Soft. Comput. 23(5), 1557\u20131572 (2019)","journal-title":"Soft. Comput."},{"key":"10_CR50","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.ins.2018.04.013","volume":"507","author":"F Min","year":"2020","unstructured":"Min, F., Zhang, Z.H., Zhai, W.J., et al.: Frequent pattern discovery with tri-partition alphabets. Inf. Sci. 507, 715\u2013732 (2020)","journal-title":"Inf. Sci."},{"key":"10_CR51","doi-asserted-by":"crossref","unstructured":"Nelwamondo, F.V., Marwala, T.: Rough set theory for the treatment of incomplete data. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1\u20136. IEEE (2007)","DOI":"10.1109\/FUZZY.2007.4295389"},{"issue":"1","key":"10_CR52","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2478\/jaiscr-2020-0004","volume":"10","author":"RK Nowicki","year":"2020","unstructured":"Nowicki, R.K., Grzanek, K., Hayashi, Y.: Rough support vector machine for classification with interval and incomplete data. J. Artif. Intell. Soft Comput. Res. 10(1), 47\u201356 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"10_CR53","first-page":"122","volume":"117","author":"J Pang","year":"2020","unstructured":"Pang, J., Guan, X., Liang, J., Wang, B., Song, P.: Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions. IJAR 117, 122\u2013147 (2020)","journal-title":"IJAR"},{"key":"10_CR54","doi-asserted-by":"crossref","unstructured":"Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer (1991)","DOI":"10.1007\/978-94-011-3534-4"},{"issue":"1","key":"10_CR55","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.ins.2006.06.006","volume":"177","author":"Z Pawlak","year":"2007","unstructured":"Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28\u201340 (2007)","journal-title":"Inf. Sci."},{"key":"10_CR56","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.ins.2014.02.073","volume":"277","author":"G Peters","year":"2014","unstructured":"Peters, G.: Rough clustering utilizing the principle of indifference. Inf. Sci. 277, 358\u2013374 (2014)","journal-title":"Inf. Sci."},{"issue":"4","key":"10_CR57","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1049\/trit.2019.0001","volume":"4","author":"H Sakai","year":"2019","unstructured":"Sakai, H., Nakata, M.: Rough set-based rule generation and apriori-based rule generation from table data sets: a survey and a combination. CAAI Trans. Intell. Technol. 4(4), 203\u2013213 (2019)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10_CR58","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.ins.2018.09.008","volume":"507","author":"H Sakai","year":"2020","unstructured":"Sakai, H., Nakata, M., Watada, J.: NIS-apriori-based rule generation with three-way decisions and its application system in SQL. Inf. Sci. 507, 755\u2013771 (2020)","journal-title":"Inf. Sci."},{"key":"10_CR59","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-319-54966-8_9","volume-title":"Thriving Rough Sets","author":"H Sakai","year":"2017","unstructured":"Sakai, H., Nakata, M., Yao, Y.: Pawlak\u2019s many valued information system, non-deterministic information system, and a proposal of new topics on information incompleteness toward the actual application. In: Wang, G., Skowron, A., Yao, Y., \u015al\u0119zak, D., Polkowski, L. (eds.) Thriving Rough Sets. SCI, vol. 708, pp. 187\u2013204. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54966-8_9"},{"issue":"11","key":"10_CR60","first-page":"1941","volume":"9","author":"B Sang","year":"2018","unstructured":"Sang, B., Guo, Y., Shi, D., et al.: Decision-theoretic rough set model of multi-source decision systems. IJMLC 9(11), 1941\u20131954 (2018)","journal-title":"IJMLC"},{"key":"10_CR61","first-page":"157","volume":"115","author":"B Sang","year":"2019","unstructured":"Sang, B., Yang, L., Chen, H., et al.: Generalized multi-granulation double-quantitative decision-theoretic rough set of multi-source information system. IJAR 115, 157\u2013179 (2019)","journal-title":"IJAR"},{"key":"10_CR62","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.knosys.2015.09.021","volume":"91","author":"AV Savchenko","year":"2016","unstructured":"Savchenko, A.V.: Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing. Knowl.-Based Syst. 91, 252\u2013262 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR63","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2019.03.030","volume":"489","author":"AV Savchenko","year":"2019","unstructured":"Savchenko, A.V.: Sequential three-way decisions in multi-category image recognition with deep features based on distance factor. Inf. Sci. 489, 18\u201336 (2019)","journal-title":"Inf. Sci."},{"issue":"Mar","key":"10_CR64","first-page":"371","volume":"9","author":"G Shafer","year":"2008","unstructured":"Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9(Mar), 371\u2013421 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10_CR65","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s10489-013-0469-x","volume":"40","author":"J Tian","year":"2014","unstructured":"Tian, J., Yu, B., Yu, D., Ma, S.: Missing data analyses: a hybrid multiple imputation algorithm using gray system theory and entropy based on clustering. Appl. Intell. 40(2), 376\u2013388 (2014)","journal-title":"Appl. Intell."},{"key":"10_CR66","first-page":"1","volume":"2017","author":"M Triff","year":"2017","unstructured":"Triff, M., Wiechert, G., Lingras, P.: Nonlinear classification, linear clustering, evolutionary semi-supervised three-way decisions: a comparison. FUZZ-IEEE 2017, 1\u20136 (2017)","journal-title":"FUZZ-IEEE"},{"key":"10_CR67","doi-asserted-by":"crossref","unstructured":"W. Grzymala-Busse, J.: Rough set strategies to data with missing attribute values. In: Proceedings of ISMIS 2005, vol. 542, pp. 197\u2013212 (2005)","DOI":"10.1007\/11539827_11"},{"key":"10_CR68","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)","journal-title":"Harv. JL Tech."},{"key":"10_CR69","unstructured":"Wang, L., Zhou, Z.H.: Cost-saving effect of crowdsourcing learning. In: IJCAI, pp. 2111\u20132117 (2016)"},{"key":"10_CR70","doi-asserted-by":"crossref","first-page":"105140","DOI":"10.1016\/j.knosys.2019.105140","volume":"189","author":"M Wang","year":"2020","unstructured":"Wang, M., Fu, K., Min, F., Jia, X.: Active learning through label error statistical methods. Knowl.-Based Syst. 189, 105140 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR71","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.ins.2019.06.015","volume":"501","author":"M Wang","year":"2019","unstructured":"Wang, M., Lin, Y., Min, F., Liu, D.: Cost-sensitive active learning through statistical methods. Inf. Sci. 501, 460\u2013482 (2019)","journal-title":"Inf. Sci."},{"key":"10_CR72","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/978-3-319-67777-4_37","volume-title":"Intelligence Science and Big Data Engineering","author":"P Wang","year":"2017","unstructured":"Wang, P., Liu, Q., Yang, X., Xu, F.: Ensemble re-clustering: refinement of hard clustering by three-way strategy. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds.) IScIDE 2017. LNCS, vol. 10559, pp. 423\u2013430. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67777-4_37"},{"key":"10_CR73","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.knosys.2018.04.029","volume":"155","author":"P Wang","year":"2018","unstructured":"Wang, P., Yao, Y.: Ce3: a three-way clustering method based on mathematical morphology. Knowl.-Based Syst. 155, 54\u201365 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR74","doi-asserted-by":"crossref","unstructured":"Yang, L., Hou, K.: A method of incomplete data three-way clustering based on density peaks. In: AIP Conference Proceedings, vol. 1967, p. 020008. AIP Publishing LLC (2018)","DOI":"10.1063\/1.5038980"},{"key":"10_CR75","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-319-60840-2_21","volume-title":"Rough Sets","author":"X Yang","year":"2017","unstructured":"Yang, X., Tan, A.: Three-way decisions based on intuitionistic fuzzy sets. In: Polkowski, L., Yao, Y., Artiemjew, P., Ciucci, D., Liu, D., \u015al\u0119zak, D., Zielosko, B. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 290\u2013299. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60840-2_21"},{"issue":"2\u20133","key":"10_CR76","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3233\/FI-2012-647","volume":"115","author":"X Yang","year":"2012","unstructured":"Yang, X., Yao, J.: Modelling multi-agent three-way decisions with decision-theoretic rough sets. Fundam. Inform. 115(2\u20133), 157\u2013171 (2012)","journal-title":"Fundam. Inform."},{"key":"10_CR77","first-page":"108","volume":"104","author":"X Yang","year":"2019","unstructured":"Yang, X., Li, T., Fujita, H., Liu, D.: A sequential three-way approach to multi-class decision. IJAR 104, 108\u2013125 (2019)","journal-title":"IJAR"},{"issue":"1","key":"10_CR78","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TFUZZ.2014.2360548","volume":"23","author":"J Yao","year":"2014","unstructured":"Yao, J., Azam, N.: Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans. Fuzzy Syst. 23(1), 3\u201315 (2014)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10_CR79","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/978-3-642-02962-2_81","volume-title":"Rough Sets and Knowledge Technology","author":"Y Yao","year":"2009","unstructured":"Yao, Y.: Three-way decision: an interpretation of rules in rough set theory. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS (LNAI), vol. 5589, pp. 642\u2013649. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02962-2_81"},{"issue":"3","key":"10_CR80","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.ins.2009.09.021","volume":"180","author":"Y Yao","year":"2010","unstructured":"Yao, Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341\u2013353 (2010)","journal-title":"Inf. Sci."},{"key":"10_CR81","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-32115-3_1","volume-title":"Rough Sets and Current Trends in Computing","author":"Y Yao","year":"2012","unstructured":"Yao, Y.: An outline of a theory of three-way decisions. In: Yao, J.T., et al. (eds.) RSCTC 2012. LNCS (LNAI), vol. 7413, pp. 1\u201317. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32115-3_1"},{"key":"10_CR82","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijar.2018.09.005","volume":"103","author":"Y Yao","year":"2018","unstructured":"Yao, Y.: Three-way decision and granular computing. Int. J. Approx. Reason. 103, 107\u2013123 (2018)","journal-title":"Int. J. Approx. Reason."},{"key":"10_CR83","doi-asserted-by":"crossref","unstructured":"Yao, Y., Deng, X.: Sequential three-way decisions with probabilistic rough sets. In: Proceedings of IEEE ICCI-CC 2011, pp. 120\u2013125. IEEE (2011)","DOI":"10.1109\/COGINF.2011.6016129"},{"key":"10_CR84","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1007\/978-3-642-10646-0_48","volume-title":"Rough Sets, Fuzzy Sets, Data Mining and Granular Computing","author":"Y Yao","year":"2009","unstructured":"Yao, Y., Lingras, P., Wang, R., Miao, D.: Interval set cluster analysis: a re-formulation. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., \u015al\u0119zak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 398\u2013405. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-10646-0_48"},{"key":"10_CR85","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/978-3-319-60840-2_22","volume-title":"Rough Sets","author":"H Yu","year":"2017","unstructured":"Yu, H.: A framework of three-way cluster analysis. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 300\u2013312. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60840-2_22"},{"key":"10_CR86","first-page":"32","volume":"115","author":"H Yu","year":"2019","unstructured":"Yu, H., Chen, Y., Lingras, P., et al.: A three-way cluster ensemble approach for large-scale data. IJAR 115, 32\u201349 (2019)","journal-title":"IJAR"},{"key":"10_CR87","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/978-3-319-11740-9_70","volume-title":"Rough Sets and Knowledge Technology","author":"H Yu","year":"2014","unstructured":"Yu, H., Su, T., Zeng, X.: A three-way decisions clustering algorithm for incomplete data. In: Miao, D., Pedrycz, W., \u015al\u0229zak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS (LNAI), vol. 8818, pp. 765\u2013776. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11740-9_70"},{"key":"10_CR88","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-319-60840-2_23","volume-title":"Rough Sets","author":"H Yu","year":"2017","unstructured":"Yu, H., Wang, X., Wang, G.: A semi-supervised three-way clustering framework for multi-view data. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 313\u2013325. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60840-2_23"},{"key":"10_CR89","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/j.ins.2018.03.009","volume":"507","author":"H Yu","year":"2020","unstructured":"Yu, H., Wang, X., Wang, G., et al.: An active three-way clustering method via low-rank matrices for multi-view data. Inf. Sci. 507, 823\u2013839 (2020)","journal-title":"Inf. Sci."},{"key":"10_CR90","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-3-642-32115-3_33","volume-title":"Rough Sets and Current Trends in Computing","author":"H Yu","year":"2012","unstructured":"Yu, H., Wang, Y.: Three-way decisions method for overlapping clustering. In: Yao, J.T., et al. (eds.) RSCTC 2012. LNCS (LNAI), vol. 7413, pp. 277\u2013286. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32115-3_33"},{"key":"10_CR91","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.knosys.2015.05.028","volume":"91","author":"H Yu","year":"2016","unstructured":"Yu, H., Zhang, C., Wang, G.: A tree-based incremental overlapping clustering method using the three-way decision theory. Knowl.-Based Syst. 91, 189\u2013203 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR92","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-319-47160-0_21","volume-title":"Rough Sets","author":"H Yu","year":"2016","unstructured":"Yu, H., Zhang, H.: A three-way decision clustering approach for high dimensional data. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 229\u2013239. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-47160-0_21"},{"key":"10_CR93","doi-asserted-by":"crossref","first-page":"122289","DOI":"10.1016\/j.physa.2019.122289","volume":"535","author":"H Yu","year":"2019","unstructured":"Yu, H., Chen, L., Yao, J., et al.: A three-way clustering method based on an improved dbscan algorithm. Phys. A 535, 122289 (2019)","journal-title":"Phys. A"},{"key":"10_CR94","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.knosys.2015.06.019","volume":"91","author":"HR Zhang","year":"2016","unstructured":"Zhang, H.R., Min, F.: Three-way recommender systems based on random forests. Knowl.-Based Syst. 91, 275\u2013286 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"10_CR95","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.ins.2016.03.019","volume":"378","author":"HR Zhang","year":"2017","unstructured":"Zhang, H.R., Min, F., Shi, B.: Regression-based three-way recommendation. Inf. Sci. 378, 444\u2013461 (2017)","journal-title":"Inf. Sci."},{"key":"10_CR96","first-page":"31","volume":"110","author":"HY Zhang","year":"2019","unstructured":"Zhang, H.Y., Yang, S.Y.: Three-way group decisions with interval-valued decision-theoretic rough sets based on aggregating inclusion measures. IJAR 110, 31\u201345 (2019)","journal-title":"IJAR"},{"key":"10_CR97","doi-asserted-by":"crossref","first-page":"105536","DOI":"10.1016\/j.asoc.2019.105536","volume":"82","author":"K Zhang","year":"2019","unstructured":"Zhang, K.: A three-way c-means algorithm. Appl. Soft Comput. 82, 105536 (2019)","journal-title":"Appl. Soft Comput."},{"key":"10_CR98","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.ins.2019.03.061","volume":"507","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Li, H., Zhou, X., et al.: Sequential three-way decision based on multi-granular autoencoder features. Inf. Sci. 507, 630\u2013643 (2020)","journal-title":"Inf. Sci."},{"issue":"4","key":"10_CR99","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1080\/00207160.2015.1124099","volume":"94","author":"T Zhang","year":"2017","unstructured":"Zhang, T., Ma, F.: Improved rough k-means clustering algorithm based on weighted distance measure with gaussian function. Int. J. Comput. Math. 94(4), 663\u2013675 (2017)","journal-title":"Int. J. Comput. Math."},{"key":"10_CR100","first-page":"85","volume":"105","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Miao, D., Wang, J., et al.: A cost-sensitive three-way combination technique for ensemble learning in sentiment classification. IJAR 105, 85\u201397 (2019)","journal-title":"IJAR"},{"key":"10_CR101","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ins.2018.10.030","volume":"477","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Zhang, Z., Miao, D., et al.: Three-way enhanced convolutional neural networks for sentence-level sentiment classification. Inf. Sci. 477, 55\u201364 (2019)","journal-title":"Inf. Sci."},{"issue":"1","key":"10_CR102","doi-asserted-by":"crossref","first-page":"211","DOI":"10.33073\/pjm-2014-027","volume":"55","author":"B Zhou","year":"2014","unstructured":"Zhou, B.: Multi-class decision-theoretic rough sets. IJAR 55(1), 211\u2013224 (2014)","journal-title":"IJAR"},{"key":"10_CR103","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/978-3-642-13059-5_6","volume-title":"Advances in Artificial Intelligence","author":"B Zhou","year":"2010","unstructured":"Zhou, B., Yao, Y., Luo, J.: A three-way decision approach to email spam filtering. In: Farzindar, A., Ke\u0161elj, V. (eds.) AI 2010. LNCS (LNAI), vol. 6085, pp. 28\u201339. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13059-5_6"},{"issue":"1","key":"10_CR104","first-page":"19","volume":"42","author":"B Zhou","year":"2014","unstructured":"Zhou, B., Yao, Y., Luo, J.: Cost-sensitive three-way email spam filtering. JIIS 42(1), 19\u201345 (2014)","journal-title":"JIIS"},{"issue":"1","key":"10_CR105","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"ZH Zhou","year":"2018","unstructured":"Zhou, Z.H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5(1), 44\u201353 (2018)","journal-title":"Natl. Sci. Rev."}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-52705-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:05:34Z","timestamp":1710266734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-52705-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030527044","9783030527051"],"references-count":105,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-52705-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCRS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rough Sets","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Havana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuba","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ijcrs.cujae.edu.cu","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"74% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.84","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}