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Information Journal Paper

Title

ON MINING FUZZY CLASSIFICATION RULES FOR IMBALANCED DATA

Pages

  1-9

Abstract

 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 asconfidence 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.

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    Cite

    APA: Copy

    RAHMANIAN, MOHSEN, MANSOORI, EGHBAL, & ZAREIAN JAHROMI, MEHDI. (2012). ON MINING FUZZY CLASSIFICATION RULES FOR IMBALANCED DATA. JOURNAL OF ADVANCES IN COMPUTER RESEARCH, 3(2), 1-9. SID. https://sid.ir/paper/328699/en

    Vancouver: Copy

    RAHMANIAN MOHSEN, MANSOORI EGHBAL, ZAREIAN JAHROMI MEHDI. ON MINING FUZZY CLASSIFICATION RULES FOR IMBALANCED DATA. JOURNAL OF ADVANCES IN COMPUTER RESEARCH[Internet]. 2012;3(2):1-9. Available from: https://sid.ir/paper/328699/en

    IEEE: Copy

    MOHSEN RAHMANIAN, EGHBAL MANSOORI, and MEHDI ZAREIAN JAHROMI, “ON MINING FUZZY CLASSIFICATION RULES FOR IMBALANCED DATA,” JOURNAL OF ADVANCES IN COMPUTER RESEARCH, vol. 3, no. 2, pp. 1–9, 2012, [Online]. Available: https://sid.ir/paper/328699/en

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