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DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.05.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery. REMOTE SENSING 2014. [DOI: 10.3390/rs61212005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2013.05.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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ALCALÁ R, NOJIMA Y, ISHIBUCHI H, HERRERA F. GUEST EDITORS' INTRODUCTION. INT J UNCERTAIN FUZZ 2012. [DOI: 10.1142/s0218488512020023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- R. ALCALÁ
- Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Granada, Spain 18071, Spain
| | - Y. NOJIMA
- Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan
| | - H. ISHIBUCHI
- Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan
| | - F. HERRERA
- Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Granada, Spain 18071, Spain
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STAVRAKOUDIS DIMITRISG, THEOCHARIS JOHNB. HANDLING HIGHLY-DIMENSIONAL CLASSIFICATION TASKS WITH HIERARCHICAL GENETIC FUZZY RULE-BASED CLASSIFIERS. INT J UNCERTAIN FUZZ 2012. [DOI: 10.1142/s0218488512400168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Many modern classification tasks are defined in highly-dimensional feature spaces. The derivation of high-performing genetic fuzzy rule-based classification systems (GFRBCSs) in such scenarios is a non-trivial task. This paper presents a framework for increasing the performance of GFRBCSs by creating a hierarchical fuzzy rule-based classifier. The proposed system is constructed through repeated invocations to a base GFRBCS procedure, considering at each step an input space fuzzy partition of a certain granularity. The best performing rules are inserted in the hierarchical rule base and the process is repeated again, considering a thicker granularity. The employed boosting scheme guides the algorithm in creating new rules to treat uncovered or misclassified patterns, thus monotonically increasing the performance of the classifier. Extensive experimental analysis in a number of real-world high-dimensional classification tasks proves the effectiveness of the proposed approach in increasing the performance of the base classifier, maintaining its interpretability to a considerable degree.
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Affiliation(s)
- DIMITRIS G. STAVRAKOUDIS
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
| | - JOHN B. THEOCHARIS
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
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