{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T04:37:33Z","timestamp":1778992653293,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811992964","type":"print"},{"value":"9789811992971","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-981-19-9297-1_26","type":"book-chapter","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T10:33:02Z","timestamp":1674124382000},"page":"367-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Classification Method for\u00a0Imbalanced Data Based on\u00a0Ant Lion Optimizer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9380-8097","authenticated-orcid":false,"given":"Mengmeng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8490-6285","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qibin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"H Guo","year":"2017","unstructured":"Guo, H., Li, Y., Jennifer, S., Gu, M., Huang, Y., Gong, B.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220\u2013239 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"26_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2907070","volume":"49","author":"P Branco","year":"2016","unstructured":"Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. 49(2), 1\u201350 (2016)","journal-title":"ACM Comput. Surv."},{"key":"26_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.inffus.2020.10.017","volume":"69","author":"C Wang","year":"2021","unstructured":"Wang, C., Deng, C., Yu, Z., Hui, D., Gong, X., Luo, R.: Adaptive ensemble of classifiers with regularization for imbalanced data classification. Inf. Fusion 69, 81\u2013102 (2021)","journal-title":"Inf. Fusion"},{"key":"26_CR4","doi-asserted-by":"publisher","first-page":"6895","DOI":"10.1007\/s00500-016-2439-9","volume":"21","author":"A Alkuhlani","year":"2017","unstructured":"Alkuhlani, A., Nassef, M., Farag, I.: Multistage feature selection approach for high-dimensional cancer data. Soft Comput. 21, 6895\u20136906 (2017)","journal-title":"Soft Comput."},{"key":"26_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-3-030-05587-5_33","volume-title":"Brain Informatics","author":"M Mousavian","year":"2018","unstructured":"Mousavian, M., Chen, J., Greening, S.: Feature selection and imbalanced data handling for depression detection. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 349\u2013358. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-05587-5_33"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Sun, J., et al.: FDHelper: assist unsupervised fraud detection experts with interactive feature selection and evaluation. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1\u201312. Association for Computing Machinery (2020)","DOI":"10.1145\/3313831.3376140"},{"key":"26_CR7","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/978-3-030-29516-5_45","volume-title":"Intelligent Systems and Applications","author":"I Al-Mandhari","year":"2020","unstructured":"Al-Mandhari, I., Guan, L., Edirisinghe, E.A.: Impact of the structure of data pre-processing pipelines on the performance of classifiers when applied to imbalanced network intrusion detection system dataset. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) IntelliSys 2019. AISC, vol. 1037, pp. 577\u2013589. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-29516-5_45"},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1111\/coin.12128","volume":"34","author":"S Sharma","year":"2018","unstructured":"Sharma, S., Somayaji, A., Japkowicz, N.: Learning over subconcepts: strategies for 1-class classification. Comput. Intell. 34, 440\u2013467 (2018)","journal-title":"Comput. Intell."},{"issue":"12","key":"26_CR9","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TKDE.2014.2312336","volume":"26","author":"X Zhang","year":"2014","unstructured":"Zhang, X., Hu, B.: A new strategy of cost-free learning in the class imbalance problem. IEEE Trans. Knowl. Data Eng. 26(12), 2872\u20132885 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, J.J., D\u00edez-Pastor, J.F., Arnaiz-Gonz\u00e1lez, l., Kuncheva, L.I.: Random balance ensembles for multiclass imbalance learning. Knowl.-Based Syst. 193, 105434 (2020)","DOI":"10.1016\/j.knosys.2019.105434"},{"key":"26_CR11","doi-asserted-by":"publisher","first-page":"81794","DOI":"10.1109\/ACCESS.2019.2923846","volume":"7","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Wang, Y., Ren, X., Zhou, H., Diao, X.: A classification method based on feature selection for imbalanced data. IEEE Access 7, 81794\u201381807 (2019)","journal-title":"IEEE Access"},{"issue":"9","key":"26_CR12","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"26_CR13","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(1), 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.ins.2020.07.014","volume":"542","author":"P Soltanzadeh","year":"2021","unstructured":"Soltanzadeh, P., Hashemzadeh, M.: RCSMOTE: range-controlled synthetic minority over-sampling technique for handling the class imbalance problem. Inf. Sci. 542, 92\u2013111 (2021)","journal-title":"Inf. Sci."},{"key":"26_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2020.100023","volume":"3\u20134","author":"VPK Turlapati","year":"2020","unstructured":"Turlapati, V.P.K., Prusty, M.R.: Outlier-smote: a refined oversampling technique for improved detection of COVID-19. Intell.-Based Med. 3\u20134, 100023 (2020)","journal-title":"Intell.-Based Med."},{"key":"26_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103500","volume":"90","author":"J Hamidzadeh","year":"2020","unstructured":"Hamidzadeh, J., Kashefi, N., Moradi, M.: Combined weighted multi-objective optimizer for instance reduction in two-class imbalanced data problem. Eng. Appl. Artif. Intell. 90, 103500 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"26_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2017.03.007","volume":"39","author":"J Li","year":"2018","unstructured":"Li, J., Fong, S., Wong, R.K., Chu, V.W.: Adaptive multi-objective swarm fusion for imbalanced data classification. Inf. Fusion 39, 1\u201324 (2018)","journal-title":"Inf. Fusion"},{"key":"26_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114372","volume":"168","author":"H Trittenbach","year":"2021","unstructured":"Trittenbach, H., Englhardt, A., B\u00f6hm, K.: An overview and a benchmark of active learning for outlier detection with one-class classifiers. Expert Syst. Appl. 168, 114372 (2021)","journal-title":"Expert Syst. Appl."},{"key":"26_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107112","volume":"103","author":"F Almaghrabi","year":"2021","unstructured":"Almaghrabi, F., Xu, D., Yang, J.: An evidential reasoning rule based feature selection for improving trauma outcome prediction. Appl. Soft Comput. 103, 107112 (2021)","journal-title":"Appl. Soft Comput."},{"key":"26_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101224","volume":"61","author":"D Effrosynidis","year":"2021","unstructured":"Effrosynidis, D., Arampatzis, A.: An evaluation of feature selection methods for environmental data. Eco. Inform. 61, 101224 (2021)","journal-title":"Eco. Inform."},{"issue":"2","key":"26_CR21","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1142\/S0218213009000135","volume":"18","author":"LJ Mena","year":"2009","unstructured":"Mena, L.J., Gonzalez, J.A.: Symbolic one-class learning from imbalanced datasets: application in medical diagnosis. Int. J. Artif. Intell. Tools 18(2), 273\u2013309 (2009)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"26_CR22","doi-asserted-by":"publisher","first-page":"13717","DOI":"10.1109\/ACCESS.2021.3051969","volume":"9","author":"CF Tsai","year":"2021","unstructured":"Tsai, C.F., Lin, W.C.: Feature selection and ensemble learning techniques in one-class classifiers: an empirical study of two-class imbalanced datasets. IEEE Access 9, 13717\u201313726 (2021)","journal-title":"IEEE Access"},{"key":"26_CR23","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.jmsy.2020.10.013","volume":"57","author":"J Lee","year":"2020","unstructured":"Lee, J., Lee, Y.C., Kim, J.T.: Fault detection based on one-class deep learning for manufacturing applications limited to an imbalanced database. J. Manuf. Syst. 57, 357\u2013366 (2020)","journal-title":"J. Manuf. Syst."},{"key":"26_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101935","volume":"108","author":"L Gao","year":"2020","unstructured":"Gao, L., Zhang, L., Liu, C., Wu, S.: Handling imbalanced medical image data: a deep-learning-based one-class classification approach. Artif. Intell. Med. 108, 101935 (2020)","journal-title":"Artif. Intell. Med."},{"key":"26_CR25","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.ins.2017.09.013","volume":"422","author":"F Li","year":"2018","unstructured":"Li, F., Zhang, X., Zhang, X., Du, C., Xu, Y., Tian, Y.: Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets. Inf. Sci. 422, 242\u2013256 (2018)","journal-title":"Inf. Sci."},{"key":"26_CR26","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.neucom.2019.06.065","volume":"366","author":"Z Wang","year":"2019","unstructured":"Wang, Z., Wang, B., Cheng, Y., Li, D., Zhang, J.: Cost-sensitive fuzzy multiple kernel learning for imbalanced problem. Neurocomputing 366, 178\u2013193 (2019)","journal-title":"Neurocomputing"},{"key":"26_CR27","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.ins.2020.12.023","volume":"554","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Duan, J., Kang, L., Qiu, G.: A hybrid data-level ensemble to enable learning from highly imbalanced dataset. Inf. Sci. 554, 157\u2013176 (2020)","journal-title":"Inf. Sci."},{"issue":"250","key":"26_CR28","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","volume":"250","author":"V L\u00f3pez","year":"2013","unstructured":"L\u00f3pez, V., Fern\u00e1ndez, A., Garc\u00eda, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250(250), 113\u2013141 (2013)","journal-title":"Inf. Sci."},{"issue":"6","key":"26_CR29","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.patrec.2013.01.003","volume":"34","author":"L Guo","year":"2013","unstructured":"Guo, L., Boukir, S.: Margin-based ordered aggregation for ensemble pruning. Pattern Recogn. Lett. 34(6), 603\u2013609 (2013)","journal-title":"Pattern Recogn. Lett."},{"key":"26_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114246","volume":"168","author":"Z Seng","year":"2021","unstructured":"Seng, Z., Kareem, S.A., Varathan, K.D.: A neighborhood undersampling stacked ensemble (NUS-SE) in imbalanced classification. Expert Syst. Appl. 168, 114246 (2021)","journal-title":"Expert Syst. Appl."},{"key":"26_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-642-28931-6_14","volume-title":"Hybrid Artificial Intelligent Systems","author":"K Napierala","year":"2012","unstructured":"Napierala, K., Stefanowski, J.: Identification of different types of minority class examples in imbalanced data. In: Corchado, E., Sn\u00e1\u0161el, V., Abraham, A., Wo\u017aniak, M., Gra\u00f1a, M., Cho, S.-B. (eds.) HAIS 2012. LNCS (LNAI), vol. 7209, pp. 139\u2013150. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28931-6_14"},{"key":"26_CR32","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.engappai.2016.10.008","volume":"57","author":"A Moayedikia","year":"2017","unstructured":"Moayedikia, A., Ong, K.L., Boo, Y.L., Yeoh, W.G., Jensen, R.: Feature selection for high dimensional imbalanced class data using harmony search. Eng. Appl. Artif. Intell. 57, 38\u201349 (2017)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"26_CR33","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","volume":"83","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80\u201398 (2015)","journal-title":"Adv. Eng. Softw."},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining, pp. 324\u2013331 (2009)","DOI":"10.1109\/CIDM.2009.4938667"},{"key":"26_CR35","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets (2018)","DOI":"10.1007\/978-3-319-98074-4"},{"key":"26_CR36","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1080\/01969722.2018.1541597","volume":"49","author":"Z Beheshti","year":"2018","unstructured":"Beheshti, Z.: BMNABC: binary multi-neighborhood artificial bee colony for high-dimensional discrete optimization problems. Cybern. Syst. 49, 452\u2013474 (2018)","journal-title":"Cybern. Syst."},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"He, X., Zhang, Q., Sun, N., Dong, Y.: Feature selection with discrete binary differential evolution. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, vol. 4, pp. 327\u2013330 (2009)","DOI":"10.1109\/AICI.2009.438"},{"issue":"8","key":"26_CR38","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","volume":"172","author":"E Emary","year":"2016","unstructured":"Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172(8), 371\u2013381 (2016)","journal-title":"Neurocomputing"},{"issue":"1","key":"26_CR39","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389\u2013422 (2002)","journal-title":"Mach. Learn."},{"key":"26_CR40","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.snb.2015.02.025","volume":"212","author":"K Yan","year":"2015","unstructured":"Yan, K., Zhang, D.: Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens. Actuators B Chem. 212, 353\u2013363 (2015)","journal-title":"Sens. Actuators B Chem."},{"issue":"2","key":"26_CR41","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1023\/A:1007452223027","volume":"30","author":"M Kubat","year":"1998","unstructured":"Kubat, M., Holte, R.C., Matwin, S.: Machine learning for the detection of oil spills in satellite radar images. Mach. Learn. 30(2), 195\u2013215 (1998)","journal-title":"Mach. Learn."},{"issue":"5","key":"26_CR42","first-page":"360","volume":"37","author":"AJ Viera","year":"2005","unstructured":"Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the kappa statistic. Fam. Med. 37(5), 360\u2013363 (2005)","journal-title":"Fam. Med."},{"key":"26_CR43","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/978-3-540-35488-8_13","volume-title":"Feature Extraction","author":"Y Chen","year":"2006","unstructured":"Chen, Y., Lin, C.: Combining SVMs with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction, pp. 315\u2013324. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/978-3-540-35488-8_13"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-9297-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T11:35:42Z","timestamp":1674128142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-9297-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811992964","9789811992971"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-9297-1_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"135","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":"62","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":"46% - 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":"2.8","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":"2-3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}