On mining fuzzy classi cation rules for imbalanced data

Fuzzy rule-based classi cation system (FRBCS) is a popular machine learning technique for classi cation purposes. One of the major issues when applying it on imbalanceddata sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended the basic FRBCS in order to decrease the side e ects of imbalanced data by employing data-mining criteria such as con dence and support. These measures are computed from information derived from data in the subspaces of each fuzzy rule. The experimental results show that the proposed method can improve the classi cation accuracy when applied on benchmark data sets

On mining fuzzy classi cation rules for imbalanced data Keywords:

Imbalanced data-sets , Fuzzy rule based classication systems , Data-mining