关键词: 少数类分类, 代价敏感学习, 遗传算法, 代价因子优化, 分类性能均衡

Abstract: Focused on the issue that cost value of cost-sensitive method in class imbalances is given traditionally based on the numbers of samples from different classes, a new quantization method that optimization process chose performance requirements as selection criterion is proposed. Performance requirements related to the cost factor [c] are the expressions for performance indicators of positive and negative class. The optimal cost value is searched by genetic algorithm based on a given performance expression. Classifier is learned by cost-sensitive boosting method with the optimal cost parameter. To meet the requirement of performance compromise, algorithm is implemented that chooses geometric mean of recall rates of positive and negative classes as selection criterion. Four algorithms are used to learn classifiers on three datasets. Compared with the traditional way of cost value given, the performance of classifiers those learned by AdaCost algorithm based on C4.5 and ZeroR increments 33.3% to 200% and [-44.4%] to [-16.7%] in TP, and [-49%] to [-15.6%] and 25% to 400% in TN. The former decreases misjudgment cases of positive class and do not cause serious misjudgment of negative class；the latter decreases serious misjudgment cases of negative class, and the recall rate of positive class is equal to or greater than 0.5, the classification performance is more balanced and stable.

Key words: minority class classification, cost sensitive learning, genetic algorithm, misclassification cost optimization, classification performance compromise