On Mining Fuzzy Classification Rules for Imbalanced Data
سال انتشار: 1391
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 521
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شناسه ملی سند علمی:
JR_JACR-3-2_001
تاریخ نمایه سازی: 16 شهریور 1395
چکیده مقاله:
Fuzzy rule-based classification system (FRBCS) is a popular machine learningtechnique for classification purposes. One of the major issues when applying it onimbalanced data sets is its biased to the majority class, such that, it performs poorlyin respect to the minority class. However many cases the minority classes are moreimportant than the majority ones. In this paper, we have extended the basic FRBCSin order to decrease the side effects of imbalanced data by employing data-miningcriteria such as confidence and support. These measures are computed frominformation derived from data in the sub-spaces of each fuzzy rule. Theexperimental results show that the proposed method can improve the classificationaccuracy when applied on benchmark data sets.
کلیدواژه ها:
نویسندگان
Mohsen Rahmanian
Department of Computer Engineering, Jahrom University, Jahrom, Iran
Eghbal Mansoori
Department of Computer Science and Eng., School of Engineering, Shiraz University, Shiraz, Iran
Mehdi Zareian Jahromi
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran