IFCRL: Interval-Intent Fuzzy Concept Re-Cognition Learning Model

The fuzzy concept serves as a crucial tool for describing phenomena and constitutes the fundamental unit of human cognition. Fuzzy concepts are characterized by their extent and intent, with the latter being comprised of continuous membership degrees. Given that human cognition often progresses from...

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Vydáno v:	IEEE transactions on fuzzy systems Ročník 32; číslo 6; s. 3581 - 3593
Hlavní autoři:	Ding, Yi, Xu, Weihua, Ding, Weiping, Qian, Yuhua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Classification Classification algorithms Clustering Cognition Cognition & reasoning Computational modeling Concept clustering concept-cognitive learning Context modeling Fuzzy systems Granular computing interval-intent Learning Mathematical models object classification Stochastic processes
ISSN:	1063-6706, 1941-0034
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Abstract	The fuzzy concept serves as a crucial tool for describing phenomena and constitutes the fundamental unit of human cognition. Fuzzy concepts are characterized by their extent and intent, with the latter being comprised of continuous membership degrees. Given that human cognition often progresses from vagueness to precision, it is imperative that the form of intent not be confined to a singular continuous value; rather, an interval possesses superior flexibility in this regard. Initial cognitive processes lack comprehensiveness in acquiring knowledge, necessitating subsequent cognitions to more accurately delineate the intended scope of a concept. Motivated by this insight, we proposed an interval-intent fuzzy concept re-cognition learning model (IFCRL). First, this model transforms fuzzy concept intent from a single continuous value into an interval-based representation, which describes the range of attribute values for all objects within the given interval. Second, in order to simulate secondary cognitive processes akin to those exhibited by humans toward phenomena, we present a concept re-cognition learning method capable of effectively scaling intervals within reasonable bounds. Third, aiming to overcome cognitive barriers and emulate imaginative processes observed in human brains, we introduce a concept clustering approach based on intent similarity which significantly reduces concept complexity while enhancing cognitive efficiency. Finally, we evaluate our classification performance using 12 datasets and experimental results demonstrate that IFCRL outperforms 14 other classification algorithms both feasibly and effectively.
AbstractList	The fuzzy concept serves as a crucial tool for describing phenomena and constitutes the fundamental unit of human cognition. Fuzzy concepts are characterized by their extent and intent, with the latter being comprised of continuous membership degrees. Given that human cognition often progresses from vagueness to precision, it is imperative that the form of intent not be confined to a singular continuous value; rather, an interval possesses superior flexibility in this regard. Initial cognitive processes lack comprehensiveness in acquiring knowledge, necessitating subsequent cognitions to more accurately delineate the intended scope of a concept. Motivated by this insight, we proposed an interval-intent fuzzy concept re-cognition learning model (IFCRL). First, this model transforms fuzzy concept intent from a single continuous value into an interval-based representation, which describes the range of attribute values for all objects within the given interval. Second, in order to simulate secondary cognitive processes akin to those exhibited by humans toward phenomena, we present a concept re-cognition learning method capable of effectively scaling intervals within reasonable bounds. Third, aiming to overcome cognitive barriers and emulate imaginative processes observed in human brains, we introduce a concept clustering approach based on intent similarity which significantly reduces concept complexity while enhancing cognitive efficiency. Finally, we evaluate our classification performance using 12 datasets and experimental results demonstrate that IFCRL outperforms 14 other classification algorithms both feasibly and effectively.
Author	Ding, Yi Qian, Yuhua Ding, Weiping Xu, Weihua
Author_xml	– sequence: 1 givenname: Yi orcidid: 0009-0005-2907-7110 surname: Ding fullname: Ding, Yi email: drying20@163.com organization: College of Artificial Intelligence, Southwest University, Chongqing, China – sequence: 2 givenname: Weihua orcidid: 0000-0001-5419-4654 surname: Xu fullname: Xu, Weihua email: chxuwh@gmail.com organization: College of Artificial Intelligence, Southwest University, Chongqing, China – sequence: 3 givenname: Weiping orcidid: 0000-0002-3180-7347 surname: Ding fullname: Ding, Weiping email: dwp9988@163.com organization: School of Information Science and Technology, Nantong University, Nantong, China – sequence: 4 givenname: Yuhua orcidid: 0000-0001-6772-4247 surname: Qian fullname: Qian, Yuhua email: jinchengqyh@126.com organization: Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
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Cites_doi	10.1023/A:1025419826662 10.1016/j.ijar.2017.01.009 10.1002/0471725293 10.1016/j.ijar.2013.10.002 10.1007/978-3-7908-1825-3_7 10.1109/TSMC.1985.6313426 10.1007/springerreference_62800 10.1016/j.ins.2019.05.009 10.1016/j.ins.2011.08.001 10.1016/j.ejor.2021.11.003 10.11919/j.issn.1002–0829.215044 10.1109/TNNLS.2023.3235800 10.1007/978-3-540-25929-9_6 10.1109/TSMCB.2009.2013334 10.1007/BF00058655 10.1016/j.fss.2007.04.023 10.1002/SERIES1345 10.1142/9789814675017_0002 10.21873/cgp.20063 10.1007/978-3-319-11740-9_67 10.1016/j.ins.2023.119818 10.1109/TSMCC.2006.871151 10.1007/978-3-540-24651-0_30 10.1016/j.ins.2014.07.029 10.1109/TSMC.2018.2882090 10.1007/978-3-642-01815-2_23 10.4018/jcini.2008040101 10.1016/0898-1221(92)90120-7 10.1007/s12559-023-10107-w 10.1007/978-3-642-59830-2 10.1109/TFUZZ.2022.3216110 10.1007/978-3-540-39964-3_62 10.1109/ICMLC.2011.6016738 10.1007/978-3-642-29892-9_9 10.1007/978-3-540-24651-0_18 10.1016/j.inffus.2018.08.007 10.1109/TCYB.2020.2980794 10.4236/jbise.2010.34053 10.1109/tnnls.2023.3333537 10.1016/j.knosys.2022.110093 10.1109/FUZZY.2011.6007623 10.1016/j.ins.2023.118998 10.1007/978-3-540-32262-7_19 10.1109/TFUZZ.2021.3128061 10.1016/j.fss.2012.03.003 10.1109/TSMCC.2012.2236648 10.1109/TKDE.2008.223 10.1007/s13042-021-01434-1
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References	ref13 ref12 ref15 ref14 ref53 ref52 ref11 ref10 ref17 ref16 ref19 ref18 Zhang (ref29) 2007; 24 ref46 ref45 ref48 ref47 ref42 ref41 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 Mi (ref32) 2018 ref40 Hu (ref50) 2005; 1 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref2 ref1 ref39 ref38 Murphy (ref44) 2006; 18 Kuncheva (ref51) 1995; 1 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27
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SubjectTerms	Algorithms Classification Classification algorithms Clustering Cognition Cognition & reasoning Computational modeling Concept clustering concept-cognitive learning Context modeling Fuzzy systems Granular computing interval-intent Learning Mathematical models object classification Stochastic processes
Title	IFCRL: Interval-Intent Fuzzy Concept Re-Cognition Learning Model
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