Data Mining Based Fuzzy Classification Algorithm for Imbalanced Data

The elegant fuzzy classification algorithm proposed by Ishibuchi et al. (I-algorithm) has achieved satisfactory performance on many well-known test data sets that have usually been carefully preprocessed. However, the algorithm does not provide satisfactory performance for the problems with imbalanced data that are often encountered in real-world applications. This paper presents an extension of the I-algorithm to E-algorithm to alleviate the effect of data imbalance. Both the I-algorithm and the E-algorithm are applied to Duke Energy outage data for power distribution systems fault cause identification. Their performance on this real-world imbalanced data set is presented, compared, and analyzed to demonstrate the improvement of the extended algorithm.