{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:37Z","timestamp":1740122677563,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Council&nbsp;of&nbsp;Scientific and Industrial Research, India","award":["09\/414(1117)\/2016-EMR-I"],"award-info":[{"award-number":["09\/414(1117)\/2016-EMR-I"]}]},{"name":"uoh-ioe","award":["F11\/9\/2019-U3(A)"],"award-info":[{"award-number":["F11\/9\/2019-U3(A)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03571-8","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T21:02:37Z","timestamp":1654549357000},"page":"4231-4256","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Approaches for coarsest granularity based near-optimal reduct computation"],"prefix":"10.1007","volume":"53","author":[{"given":"Abhimanyu","family":"Bar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4914-035X","authenticated-orcid":false,"given":"P. S. V. S. Sai","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"issue":"5","key":"3571_CR1","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. International journal of computer & information sciences 11(5):341\u2013356","journal-title":"International journal of computer & information sciences"},{"key":"3571_CR2","doi-asserted-by":"publisher","unstructured":"Yao Y, Zhao Y, Wang J (2008) On reduct construction algorithms. In: Transactions on computational science II. https:\/\/doi.org\/10.1007\/11795131_43. Springer, pp 100\u2013117","DOI":"10.1007\/11795131_43"},{"key":"3571_CR3","doi-asserted-by":"publisher","unstructured":"Xu B, Chen H, Zhu W, Zhu X (2013) Multi-objective cost-sensitive attribute reduction. In: 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA\/NAFIPS). https:\/\/doi.org\/10.1109\/IFSA-NAFIPS.2013.6608602, IEEE, pp 1377\u20131381","DOI":"10.1109\/IFSA-NAFIPS.2013.6608602"},{"key":"3571_CR4","doi-asserted-by":"crossref","unstructured":"Zhao H, Min F, Zhu W (2011) Test-cost-sensitive attribute reduction based on neighborhood rough set. In: 2011 IEEE International conference on granular computing, IEEE, pp 802\u2013806","DOI":"10.1109\/GRC.2011.6122701"},{"key":"3571_CR5","doi-asserted-by":"publisher","unstructured":"Inuiguchi M (2017) Attribute importance degrees corresponding to several kinds of attribute reduction in the setting of the classical rough sets. In: Fuzzy Sets, Rough Sets, Multisets and Clustering. https:\/\/doi.org\/10.1007\/978-3-319-47557-8_14, vol 671. Springer, pp 241\u2013255","DOI":"10.1007\/978-3-319-47557-8_14"},{"key":"3571_CR6","doi-asserted-by":"publisher","first-page":"2576","DOI":"10.1016\/j.procs.2020.09.315","volume":"176","author":"B Zielosko","year":"2020","unstructured":"Zielosko B, Sta\u0144czyk U (2020) Reduct-based ranking of attributes. Procedia Computer Science 176:2576\u20132585. https:\/\/doi.org\/10.1016\/j.procs.2020.09.315","journal-title":"Procedia Computer Science"},{"key":"3571_CR7","doi-asserted-by":"publisher","unstructured":"Bazan JG, Nguyen HS, Nguyen SH, Synak P, Wr\u00f3blewski J (2000) Rough set algorithms in classification problem. In: Rough set methods and applications. https:\/\/doi.org\/10.1007\/978-3-7908-1840-6_3, vol 56. Springer, pp 49\u201388","DOI":"10.1007\/978-3-7908-1840-6_3"},{"key":"3571_CR8","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.asoc.2018.01.040","volume":"65","author":"AK Das","year":"2018","unstructured":"Das A K, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65:400\u2013411. https:\/\/doi.org\/10.1016\/j.asoc.2018.01.040","journal-title":"Appl Soft Comput"},{"key":"3571_CR9","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ins.2013.07.033","volume":"255","author":"R Jensen","year":"2014","unstructured":"Jensen R, Tuson A, Shen Q (2014) Finding rough and fuzzy-rough set reducts with sat. Inf Sci 255:100\u2013120. https:\/\/doi.org\/10.1016\/j.ins.2013.07.033","journal-title":"Inf Sci"},{"issue":"5","key":"3571_CR10","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1109\/TFUZZ.2020.2965899","volume":"28","author":"A Kumar","year":"2020","unstructured":"Kumar A, Prasad PSVSS (2020) Scalable fuzzy rough set reduct computation using fuzzy min?max neural network preprocessing. IEEE Trans Fuzzy Syst 28(5):953\u2013964. https:\/\/doi.org\/10.1109\/TFUZZ.2020.2965899","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"3571_CR11","doi-asserted-by":"publisher","unstructured":"Sai Prasad PSVS, Rao CR (2011) Extensions to iquickreduct. In: MIWAI. https:\/\/doi.org\/10.1007\/978-3-642-25725-4_31, vol 7080. Springer, pp 351\u2013362","DOI":"10.1007\/978-3-642-25725-4_31"},{"issue":"6","key":"3571_CR12","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.1109\/18.720554","volume":"44","author":"A Barron","year":"1998","unstructured":"Barron A, Rissanen J, Yu B (1998) The minimum description length principle in coding and modeling. IEEE Trans Inf Theory 44(6):2743\u20132760","journal-title":"IEEE Trans Inf Theory"},{"issue":"2","key":"3571_CR13","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/18.825807","volume":"46","author":"PMB Vit\u00e1nyi","year":"2000","unstructured":"Vit\u00e1nyi PMB, Li M (2000) Minimum description length induction, bayesianism, and kolmogorov complexity. IEEE Transactions on information theory 46(2):446\u2013464","journal-title":"IEEE Transactions on information theory"},{"key":"3571_CR14","doi-asserted-by":"publisher","unstructured":"Choroma\u0144ski M, Grze\u015b T, Ho\u0144ko P (2020) Breadth search strategies for finding minimal reducts: towards hardware implementation. Neural Computing & Applications, 32(18). https:\/\/doi.org\/10.1007\/s00521-020-04833-7","DOI":"10.1007\/s00521-020-04833-7"},{"key":"3571_CR15","unstructured":"Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: A tutorial. Rough fuzzy hybridization: A new trend in decision-making, pp 3\u201398"},{"key":"3571_CR16","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.patrec.2020.07.004","volume":"138","author":"V Rodr\u00edguez-Diez","year":"2020","unstructured":"Rodr\u00edguez-Diez V, Mart\u00ednez-Trinidad JF, Carrasco-Ochoa JA, Lazo-Cort\u00e9s MS, Olvera-L\u00f3pez JA (2020) Minreduct: A new algorithm for computing the shortest reducts. Pattern Recogn Lett 138:177\u2013184. https:\/\/doi.org\/10.1016\/j.patrec.2020.07.004","journal-title":"Pattern Recogn Lett"},{"key":"3571_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-7975-9_21","volume-title":"The discernibility matrices and functions in information systems","author":"A Skowron","year":"1992","unstructured":"Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. Springer Netherlands. https:\/\/doi.org\/10.1007\/978-94-015-7975-9_21"},{"issue":"2","key":"3571_CR18","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s101150050007","volume":"2","author":"JA Starzyk","year":"2000","unstructured":"Starzyk JA, Nelson DE, Sturtz K (2000) A mathematical foundation for improved reduct generation in information systems. Knowl Inf Syst 2(2):131\u2013146. https:\/\/doi.org\/10.1007\/s101150050007https:\/\/doi.org\/10.1007\/s101150050007","journal-title":"Knowl Inf Syst"},{"key":"3571_CR19","unstructured":"Wroblewski J (1995) Finding minimal reducts using genetic algorithms. In: Proccedings of the second annual join conference on infromation science, vol 2, pp 186\u2013189"},{"key":"3571_CR20","doi-asserted-by":"publisher","unstructured":"Bar A, Kumar A, Prasad PSVSS (2019) Finding optimal rough set reduct with a\u2217 search algorithm. In: Lecture notes in computer science. https:\/\/doi.org\/10.1007\/978-3-030-34869-4_35, vol 11941. Springer International Publishing, pp 317\u2013327","DOI":"10.1007\/978-3-030-34869-4_35"},{"key":"3571_CR21","doi-asserted-by":"crossref","unstructured":"Bar A, Kumar A, Sai Prasad PSVS (2022) Coarsest granularity-based optimal reduct using a* search. Granular Computing, pp 1\u201322","DOI":"10.1007\/s41066-022-00313-6"},{"key":"3571_CR22","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-59904-552-8 10.4018\/978-1-59904-552-8","volume-title":"Rough computing: Theories, technologies and applications","author":"AE H.","year":"2007","unstructured":"H. AE, Hassanien AE, Suraj Z, Slezak D, Lingras P (2007) Rough computing: Theories, technologies and applications. IGI Global, Hershey, PA, USA. https:\/\/doi.org\/10.4018\/978-1-59904-552-8https:\/\/doi.org\/10.4018\/978-1-59904-552-8"},{"issue":"8","key":"3571_CR23","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1109\/TKDE.2011.101","volume":"24","author":"K Shehzad","year":"2011","unstructured":"Shehzad K (2011) Edisc: a class-tailored discretization technique for rule-based classification. IEEE Trans Knowl Data Eng 24(8):1435\u20131447. https:\/\/doi.org\/10.1109\/TKDE.2011.101","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3571_CR24","doi-asserted-by":"publisher","unstructured":"Pawalk Z (1991) Rough sets: theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers, https:\/\/doi.org\/10.1007\/978-94-011-3534-4","DOI":"10.1007\/978-94-011-3534-4"},{"key":"3571_CR25","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.knosys.2017.12.014","volume":"143","author":"C Gao","year":"2018","unstructured":"Gao C, Lai Z, Zhou J, Zhao C, Miao D (2018) Maximum decision entropy-based attribute reduction in decision-theoretic rough set model. Knowl-Based Syst 143:179\u2013191. https:\/\/doi.org\/10.1016\/j.knosys.2017.12.014","journal-title":"Knowl-Based Syst"},{"issue":"9","key":"3571_CR26","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1080\/088395101753210773","volume":"15","author":"A Chouchoulas","year":"2001","unstructured":"Chouchoulas A, Shen Q (2001) Rough set-aided keyword reduction for text categorization. Appl Artif Intell 15(9):843\u2013873. https:\/\/doi.org\/10.1007\/978-3-540-48061-7_16","journal-title":"Appl Artif Intell"},{"issue":"12","key":"3571_CR27","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1109\/TKDE.2004.96","volume":"16","author":"R Jensen","year":"2004","unstructured":"Jensen R, Shen Q (2004) Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans Knowl Data Eng 16(12):1457\u20131471. https:\/\/doi.org\/10.1109\/TKDE.2004.96","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3571_CR28","doi-asserted-by":"crossref","unstructured":"Han J, Hu X, Lin T Y (2004) Feature subset selection based on relative dependency between attributes. In: International conference on rough sets and current trends in computing, vol 3066, Springer, pp 176\u2013185","DOI":"10.1007\/978-3-540-25929-9_20"},{"key":"3571_CR29","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.knosys.2015.02.002","volume":"81","author":"Y Chen","year":"2015","unstructured":"Chen Y, Zhu Q, Xu H (2015) Finding rough set reducts with fish swarm algorithm. Knowl-Based Syst 81:22\u201329. https:\/\/doi.org\/10.1016\/j.knosys.2015.02.002","journal-title":"Knowl-Based Syst"},{"key":"3571_CR30","doi-asserted-by":"crossref","unstructured":"Nilsson NJ (2009) The quest for artificial intelligence. Cambridge University Press","DOI":"10.1017\/CBO9780511819346"},{"issue":"2","key":"3571_CR31","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","volume":"4","author":"PE Hart","year":"1968","unstructured":"Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics 4(2):100\u2013107","journal-title":"IEEE transactions on Systems Science and Cybernetics"},{"issue":"9-10","key":"3571_CR32","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1016\/j.artint.2010.04.018","volume":"174","author":"Y Qian","year":"2010","unstructured":"Qian Y, Liang J, Pedrycz W, Dang C (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artificial intelligence 174(9-10):597\u2013618. https:\/\/doi.org\/10.1016\/j.artint.2010.04.018","journal-title":"Artificial intelligence"},{"key":"3571_CR33","unstructured":"Jensen R, Shen Q (2003) Finding rough set reducts with ant colony optimization. In: Proceedings of UKCI-2003, vol 1, pp 15\u201322"},{"issue":"4","key":"3571_CR34","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.patrec.2006.09.003","volume":"28","author":"X Wang","year":"2007","unstructured":"Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and pso. Pattern recognition letters 28(4):459\u2013471. https:\/\/doi.org\/10.1016\/j.patrec.2006.09.003","journal-title":"Pattern recognition letters"},{"key":"3571_CR35","doi-asserted-by":"publisher","unstructured":"Divya UV, Prasad PSVSS (2018) Hashing supported iterative mapreduce based scalable sbe reduct computation. In: International conference on distributed computing and internet technology. https:\/\/doi.org\/10.1007\/978-3-319-72344-0_13, vol 10722, Springer, pp 163\u2013170","DOI":"10.1007\/978-3-319-72344-0_13"},{"key":"3571_CR36","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.knosys.2015.05.017","volume":"91","author":"X Jia","year":"2016","unstructured":"Jia X, Shang L, Zhou B, Yao Y (2016) Generalized attribute reduct in rough set theory. Knowl-Based Syst 91:204\u2013218","journal-title":"Knowl-Based Syst"},{"key":"3571_CR37","unstructured":"Arel-Bundock V (2012) Rdatasets: An archive of datasets distributed with r. https:\/\/vincentarelbundock.github.io\/Rdatasets\/datasets.html"},{"key":"3571_CR38","unstructured":"Dua D, Karra Taniskidou E (2017) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. https:\/\/doi.org\/http:\/\/archive.ics.uci.edu\/ml"},{"key":"3571_CR39","unstructured":"Kim HJ (2010) mdlp: Discretization using the minimum description length principle. https:\/\/rdrr.io\/cran\/discretization\/"},{"key":"3571_CR40","doi-asserted-by":"crossref","unstructured":"Bazan JG, Szczuka M (2005) The rough set exploration system. In: Transactions on rough sets III. https:\/\/www.mimuw.edu.pl\/~szczuka\/rses\/. Springer, pp 37\u201356","DOI":"10.1007\/11427834_2"},{"key":"3571_CR41","doi-asserted-by":"publisher","unstructured":"Karpinski M, Schudy W (2011) Approximation schemes for the betweenness problem in tournaments and related ranking problems. In: Approximation, randomization, and combinatorial optimization. Algorithms and techniques. https:\/\/doi.org\/10.1007\/3-540-44666-4, vol 6845. Springer, pp 277\u2013288","DOI":"10.1007\/3-540-44666-4"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03571-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03571-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03571-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T06:49:38Z","timestamp":1675234178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03571-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3571"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03571-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,6,6]]},"assertion":[{"value":"31 March 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}