Barrenechea, Edurne; Bustince, Humberto; Fernandez, Javier; Paternain, Daniel; Sanz, José Antonio Using the Choquet integral in the fuzzy reasoning method of fuzzy rule-based classification systems. (English) Zbl 1442.68229 Axioms 2, No. 2, 208-223 (2013). Summary: In this paper we present a new fuzzy reasoning method in which the Choquet integral is used as aggregation function. In this manner, we can take into account the interaction among the rules of the system. For this reason, we consider several fuzzy measures, since it is a key point on the subsequent success of the Choquet integral, and we apply the new method with the same fuzzy measure for all the classes. However, the relationship among the set of rules of each class can be different and therefore the best fuzzy measure can change depending on the class. Consequently, we propose a learning method by means of a genetic algorithm in which the most suitable fuzzy measure for each class is computed. From the obtained results it is shown that our new proposal allows the performance of the classical fuzzy reasoning methods of the winning rule and additive combination to be enhanced whenever the fuzzy measure is appropriate for the tackled problem. Cited in 9 Documents MSC: 68T37 Reasoning under uncertainty in the context of artificial intelligence 28E10 Fuzzy measure theory 68T05 Learning and adaptive systems in artificial intelligence 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) Keywords:fuzzy rule-based classification systems; Choquet integral; fuzzy measure; genetic algorithm Software:KEEL; JStatCom PDFBibTeX XMLCite \textit{E. Barrenechea} et al., Axioms 2, No. 2, 208--223 (2013; Zbl 1442.68229) Full Text: DOI References: [1] Duda, Pattern Classification (2001) [2] Alpaydin, Introduction to Machine Learning (2010) [3] Ishibuchi, Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (2004) · Zbl 1060.68102 [4] DOI: 10.1109/TPWRD.2010.2042625 [5] DOI: 10.1142/S0218488596000159 · Zbl 1232.68109 [6] DOI: 10.1109/21.156575 · Zbl 0775.68028 [7] DOI: 10.1016/0165-0114(92)90032-Y [8] Chi, Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition (1996) · Zbl 0942.68001 [9] DOI: 10.1016/0165-0114(95)00095-X · Zbl 05473550 [10] Beliakov, Aggregation Functions: A Guide for Practitioners. What is an aggregation function pp 1– (2007) [11] Calvo, Aggregation Operators New Trends and Applications: Aggregation Operators: Properties, Classes and Construction Methods pp 3– (2002) [12] DOI: 10.5802/aif.53 · Zbl 0064.35101 [13] DOI: 10.3390/axioms1010009 · Zbl 1293.28008 [15] DOI: 10.1038/scientificamerican0792-66 [16] DOI: 10.1007/s00500-008-0323-y · Zbl 05533424 [17] Alcalá-Fdez, KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework, J. Mult.-Valued Logic Soft Comput. 17 pp 255– (2011) [18] Demšar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7 pp 1– (2006) [19] DOI: 10.1016/j.ins.2009.12.010 · Zbl 05758514 [20] DOI: 10.1016/S0019-9958(65)90241-X · Zbl 0139.24606 [21] DOI: 10.1109/91.940964 [22] DOI: 10.1016/S0888-613X(00)88942-2 · Zbl 05466799 [23] DOI: 10.1109/21.199466 [24] DOI: 10.1109/TFUZZ.2004.841738 · Zbl 05452520 [25] Eshelman, The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination, Foundations of Genetic Algorithms pp 265– (1991) [26] DOI: 10.1002/int.10091 · Zbl 1048.68067 [27] DOI: 10.1109/TSMCC.2011.2161285 [28] DOI: 10.1016/j.patcog.2011.01.017 · Zbl 05937848 [29] Bardossy, Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems (1995) [30] DOI: 10.1016/j.ins.2010.06.018 · Zbl 05781099 [31] DOI: 10.1007/s00500-008-0392-y · Zbl 05586575 [32] Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures (2006) [33] DOI: 10.1214/aoms/1177704575 · Zbl 0112.10303 [34] Holm, A simple sequentially rejective multiple test procedure, Scand. J. Stat. 6 pp 65– (1979) · Zbl 0402.62058 [35] García, An extension on ”statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons, J. Mach. Learn. Res. 9 pp 2677– (2008) · Zbl 1225.68178 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.