关键词: 模糊支持向量机, 加权模糊支持向量机, 分类超平面, 模糊隶属度, 平衡调节因子

Abstract: In view of the classification of imbalanced data set, a weighted fuzzy support vector machine is proposed, making use of the balanced adjustment factor and the fuzzy membership based on the features of samples. Firstly, it trains the classification hyperplane by traditional support vector machine and gets the fuzzy membership of every sample to be considered as the contribution rate of every sample to eliminate the error caused by noises and outliers and subtract the number of samples in a certain extent. Subsequently, it computes the balanced adjustment factor to alleviate the migration of hyperplane. Ultimately, experiments on a number of real-world data sets even including the data sets are imbalanced show that the proposed weighted fuzzy support vector machine algorithm is scalable and outperforms the existing fuzzy support vector machine as well as the typical support vector machine counterparts.

Key words: fuzzy support vector machine, weighted fuzzy support vector machine, classification hyperplane, fuzzy membership, balanced adjustment factor