Dense fuzzy support vector machine to binary classification for imbalanced data

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Majority classes are easily to be found in imbalance datasets, instead, minority classes are hard to be paid attention to due to the number of is rare. However, most existing classifiers are better at exploring majority classes, resulting in that classification results are unfair. To address this issue of binary classification for imbalance data, this paper proposes a novel fuzzy support vector machine. The thought is that we trained two support vector machines to learn the majority class and the minority class, respectively. Then, the proposed fuzzy is used to estimate the assistance provided by instance points for the training of the support vector machines. Finally, it can be judged for unknown instance points through evaluating that they provided the assistance to the training of the support vector machines. Results on the ten UCI datasets show that the class accuracy of the proposed method is 0.747 when the imbalanced ratio between the classes reaches 87.8. Compare with the competitors, the proposed method wins over them in classification performance. We find that aiming at the classification of imbalanced data, the complexity of data distribution has negative effects on classification results, while fuzzy can resist these negative effects. Moreover, fuzzy can assist those classifiers to gain superior classification boundaries.