A fuzzy support vector machine based on environmental membership and its application to motor fault classification

Abstract

To weaken the effects of the outliers or noise in classification, a fuzzy support vector machine (FSVM) based on environmental fuzzy membership is proposed. The environmental fuzzy membership considers not only the number of the similar samples nearby but also the distribution of the samples nearby. As more information of the samples is considered, the reliability and robustness of the FSVM is further enhanced, which can improve the classification performance, especially for overlapping samples. The classification performance of the proposed method is validated by numerical case studies, an experimental study for a breast cancer dataset, and an application to motor fault classification. Compared with the FSVM based on the k-nearest neighbor algorithm, the proposed method obtains more robust and accurate classification rates in all case studies.