Accuracy improvement of genetic fuzzy rule selection with candidate rule addition and membership tuning
Yusuke Nojima, Yutaka Kaisho, Hisao Ishibuchi · 2010
Data mining is a very active and rapidly growing research area in the field of computer science. Its goal is to obtain useful knowledge for users from a database. Association rule mining from a database is one of the most well-known data mining techniques. In general, a large number of if-then rules are extracted by specifying minimum support and confidence levels. They are, however, too complicated as knowledge for users to understand many rules at one time. Multiobjective genetic fuzzy rule selection from Pareto-optimal and near Pareto-optimal rules is a promising approach which can obtain an accurate and simple rule set by considering the accuracy maximization and the complexity minimization. In this paper, we propose two extensions of multiobjective genetic fuzzy rule selection for designing more accurate fuzzy rule-based classifiers. One extension is to add compatible rules with misclassified patterns into candidate rules for genetic fuzzy rule selection. The other is to tune membership functions after genetic fuzzy rule selection. We examine the effects of these extensions through computational experiments on imbalanced data sets.