关键词: 文本分类, 不均衡数据集, 数据挖掘, 样本重采样, 熵值法

Abstract: In order to improve the classification efficiency of unbalanced data sets,this paper proposes a classification model.The sample sampling and classification algorithm are optimized.A new sample set is generated by cyclic sampling of the few samples of the decision boundary,combined with the small sample sets synthesized outside the boundary of the decision-making,then the importance of the sample is improved.Aiming at the problem of hyperplane offset in classification of imbalanced data sets by traditional ε-Support Vector Machine(ε-SVM),the positive and negative penalty coefficients and the mixed kernel function are introduced.The objective entropy value method is used to select the penalty coefficients and the performance of the classification algorithm is improved.Experimental results show that compared with the standard SVM algorithm,the classification is better in the classification of imbalanced data sets,the average F-measure value is increased by 18.1%,and the better classification results are achieved.

Key words: text categorization, imbalanced data set, data mining, sample resampling, entropy method