On Mining Fuzzy Classification Rules for Imbalanced Data

نویسندگانMohsen Rahmanian - Eghbal Mansoori - Mehdi Zareian Jahromi
شماره صفحات1-9
شماره سریال2
شماره مجلد3
نوع مقالهFull Paper
تاریخ انتشار2012-01-01
رتبه نشریهعلمی - پژوهشی
نوع نشریهچاپی
کشور محل چاپایران
نمایه نشریهISC

چکیده مقاله

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data 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 effects of imbalanced data by employing data-mining criteria such as confidence and support. These measures are computed from information derived from data in the sub-spaces of each fuzzy rule. The experimental results show that the proposed method can improve the classification accuracy when applied on benchmark data sets.