A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning
Metadatos
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Materia
Associative classification Classification Data mining Fuzzy association rules Genetic algorithms (GAs) Genetic fuzzy rule selection High-dimensional problems
Date
2011Referencia bibliográfica
Alcala-Fdez, J., Alcala, R., & Herrera, F. (2011). A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Transactions on Fuzzy Systems, 19(5), 857-872. doi:10.1109/TFUZZ.2011.2147794
Patrocinador
Spanish Government TIN2008-06681-C06-01Résumé
The inductive learning of fuzzy rule-based classification systems suffers from exponential growth of the fuzzy rule
search space when the number of patterns and/or variables becomes high. This growth makes the learning process more difficult
and, in most cases, it leads to problems of scalability (in terms of the
time and memory consumed) and/or complexity (with respect to
the number of rules obtained and the number of variables included
in each rule). In this paper, we propose a fuzzy association rulebased classification method for high-dimensional problems, which
is based on three stages to obtain an accurate and compact fuzzy
rule-based classifier with a low computational cost. This method
limits the order of the associations in the association rule extraction and considers the use of subgroup discovery, which is based
on an improved weighted relative accuracy measure to preselect
the most interesting rules before a genetic postprocessing process
for rule selection and parameter tuning. The results that are obtained more than 26 real-world datasets of different sizes and with
different numbers of variables demonstrate the effectiveness of the
proposed approach.