针对非平衡数据分类的新型模糊SVM模型

doi:10.3969/j.issn.1001-2400.2015.05.021

Abstract

Abstract:

The paper proposes a new fuzzy SVM, called CI-FSVM(Class Imbalance Fuzzy Support Vector Machine) short for which is based on imbalanced datasets classification. By improving penalty functions, we reduce the sensitivity of the model for imbalanced datasets with “overlap”. In addition, the parameters in SVM models are optimized by the grid-parameter-search algorithm. The results show that the CI-FSVM has a better effect in imbalanced datasets classification compared with other models. It not only has a higher overall accuracy, but also improves are judgment accuracy when dealing with the minority classifications.

Key words: support vector machine, classification, imbalanced datasets, noise samples, penalty function

CAI Yanyan;SONG Xiaodong. New fuzzy SVM model used in imbalanced datasets[J].J4, 2015, 42(5): 120-124+160.

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New fuzzy SVM model used in imbalanced datasets

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