{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:28:08Z","timestamp":1742938088437,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031509582"},{"type":"electronic","value":"9783031509599"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-50959-9_5","type":"book-chapter","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T10:02:35Z","timestamp":1703930555000},"page":"62-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Acceleration Method for\u00a0Attribute Reduction Based on\u00a0Attribute Synthesis"],"prefix":"10.1007","author":[{"given":"Chengzhi","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taihua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuhao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xibei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,31]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers (1992)","DOI":"10.1007\/978-94-011-3534-4_7"},{"issue":"12","key":"5_CR2","doi-asserted-by":"publisher","first-page":"3963","DOI":"10.1007\/s13042-022-01634-3","volume":"13","author":"JB Wang","year":"2022","unstructured":"Wang, J.B., Wu, W.Z., Tan, A.H.: Multi-granulation-based knowledge discovery in incomplete generalized multi-scale decision systems. Int. J. Mach. Learn. Cybernet. 13(12), 3963\u20133979 (2022)","journal-title":"Int. J. Mach. Learn. Cybernet."},{"issue":"22","key":"5_CR3","doi-asserted-by":"publisher","first-page":"4384","DOI":"10.1016\/j.ins.2010.07.010","volume":"180","author":"QH Hu","year":"2010","unstructured":"Hu, Q.H., An, S., Yu, D.R.: Soft fuzzy rough sets for robust feature evaluation and selection. Inf. Sci. 180(22), 4384\u20134400 (2010)","journal-title":"Inf. Sci."},{"issue":"1","key":"5_CR4","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ijar.2018.01.008","volume":"97","author":"YH Qian","year":"2018","unstructured":"Qian, Y.H., Liang, X.Y., Wang, Q., et al.: Local rough set: a solution to rough data analysis in big data. Int. J. Approximate Reasonging 97(1), 38\u201363 (2018)","journal-title":"Int. J. Approximate Reasonging"},{"issue":"1","key":"5_CR5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.knosys.2017.02.019","volume":"123","author":"HR Ju","year":"2017","unstructured":"Ju, H.R., Li, H.X., Yang, X.B., et al.: Cost-sensitive rough set: a multi-granulation approach. Knowl.-Based Syst. 123(1), 137\u2013153 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.ins.2017.08.038","volume":"418\u2013419","author":"YY Yao","year":"2017","unstructured":"Yao, Y.Y., Zhang, X.Y.: Class-specific attribute reducts in rough set theory. Inf. Sci. 418\u2013419, 601\u2013618 (2017)","journal-title":"Inf. Sci."},{"issue":"2","key":"5_CR7","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1109\/69.842271","volume":"12","author":"R Slowinski","year":"2000","unstructured":"Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Trans. Knowl. Data Eng. 12(2), 331\u2013336 (2000)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ijar.2019.12.001","volume":"118","author":"TH Xu","year":"2020","unstructured":"Xu, T.H., Wang, G.Y., Yang, J.: Finding strongly connected components of simple digraphs based on granulation strategy. Int. J. Approximate Reasoning 118, 64\u201378 (2020)","journal-title":"Int. J. Approximate Reasoning"},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1080\/03081079008935107","volume":"17","author":"D Dubois","year":"1990","unstructured":"Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gener. Syst. 17, 191\u2013209 (1990)","journal-title":"Int. J. Gener. Syst."},{"key":"5_CR10","unstructured":"Lin, T.Y.: Granular Computing on binary relations I: data mining and neighborhood systems. In: Skoworn, A., Polkowshi, L. (eds.) Rough Sets in Knowledge Discovery, pp. 107\u2013121. Physica-Verlag (1998)"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.eswa.2006.10.043","volume":"34","author":"QH Hu","year":"2008","unstructured":"Hu, Q.H., Yu, D.R., Xie, Z.X.: Neighborhood classifiers. Expert Syst. Appl. 34, 866\u2013876 (2008)","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"5_CR12","doi-asserted-by":"publisher","first-page":"3645","DOI":"10.1007\/s13042-022-01618-3","volume":"13","author":"ZC Gong","year":"2022","unstructured":"Gong, Z.C., Liu, Y.X., Xu, T.H., et al.: Unsupervised attribute reduction: improving effectiveness and efficiency. Int. J. Mach. Learn. Cybernet. 13(11), 3645\u20133662 (2022)","journal-title":"Int. J. Mach. Learn. Cybernet."},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.ins.2022.07.063","volume":"609","author":"Y Fang","year":"2022","unstructured":"Fang, Y., Cao, X.M., Wang, X., et al.: Three-way sampling for rapid attribute reduction. Inf. Sci. 609, 26\u201345 (2022)","journal-title":"Inf. Sci."},{"issue":"1","key":"5_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-022-00076-7","volume":"15","author":"ZJ Wu","year":"2022","unstructured":"Wu, Z.J., Mei, Q.Y., Zhang, Y., et al.: A distributed attribute reduction algorithm for high-dimensional data under the spark framework. Int. J. Comput. Intell. Syst. 15(1), 1\u201314 (2022)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"5_CR15","unstructured":"Yang, T.L., Li, Z.W., Li, J.J.: Attribute reduction for set-valued data based on prediction label. Int. J. Gener. Syst. 1\u201331 (2023)"},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.ijar.2022.09.007","volume":"151","author":"C Gao","year":"2022","unstructured":"Gao, C., Zhou, J., Xing, J., et al.: Parameterized maximum-entropy-based three-way approximate attribute reduction. Int. J. Approximate Reasoning 151, 85\u2013100 (2022)","journal-title":"Int. J. Approximate Reasoning"},{"issue":"9","key":"5_CR17","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.3390\/sym14091828","volume":"14","author":"Q Chen","year":"2022","unstructured":"Chen, Q., Xu, T.H., Chen, J.J.: Attribute reduction based on lift and random sampling. Symmetry 14(9), 1828 (2022)","journal-title":"Symmetry"},{"issue":"4","key":"5_CR18","doi-asserted-by":"publisher","first-page":"553","DOI":"10.3390\/math10040553","volume":"10","author":"WW Yan","year":"2022","unstructured":"Yan, W.W., Ba, J., Xu, T.H., Yu, H.L., Shi, J.L., Han, B.: Beam-influenced attribute selector for producing stable reduct. Mathematics 10(4), 553 (2022)","journal-title":"Mathematics"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.knosys.2019.04.014","volume":"177","author":"ZH Jiang","year":"2019","unstructured":"Jiang, Z.H., Yang, X.B., Yu, H.L., et al.: Accelerator for multi-granularity attribute reduction. Knowl.-Based Syst. 177, 145\u2013158 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"5_CR20","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3390\/info9110282","volume":"9","author":"Y Gao","year":"2018","unstructured":"Gao, Y., Chen, X.J., Yang, X.B., et al.: Neighborhood attribute reduction: a multicriterion strategy based on sample selection. Information 9, 282\u2013302 (2018)","journal-title":"Information"},{"key":"5_CR21","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1109\/TKDE.2011.149","volume":"24","author":"QH Hu","year":"2012","unstructured":"Hu, Q.H., Che, X.J., Zhang, L., et al.: Rank entropy based decision trees for monotonic classification. IEEE Trans. Knowl. Data Eng. 24, 2052\u20132064 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.knosys.2018.11.034","volume":"165","author":"KY Liu","year":"2019","unstructured":"Liu, K.Y., Yang, X.B., Yu, H.L., et al.: Rough set based semi-supervised feature selection via ensemble selector. Knowl.-Based Syst. 165, 282\u2013296 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"5_CR23","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ijar.2018.10.014","volume":"104","author":"C Gao","year":"2019","unstructured":"Gao, C., Lai, Z.H., Zhou, J., et al.: Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction. Int. J. Approximate Reasoning 104, 9\u201324 (2019)","journal-title":"Int. J. Approximate Reasoning"},{"key":"5_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ijar.2018.11.010","volume":"105","author":"XB Yang","year":"2019","unstructured":"Yang, X.B., Liang, S.C., Yu, H.L., et al.: Pseudo-label neighborhood rough set: measures and attribute reductions. Int. J. Approximate Reasoning 105, 115\u2013129 (2019)","journal-title":"Int. J. Approximate Reasoning"}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50959-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T10:03:41Z","timestamp":1703930621000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50959-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031509582","9783031509599"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50959-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 December 2023","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":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ijcrs2023.agh.edu.pl\/","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":"Springer EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","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":"43","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":"52% - 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":"3","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":"5","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)"}}]}}