A Hybrid Feature Selection Algorithm For Classification Unbalanced Data Processsing
A Hybrid Feature Selection Algorithm For Classification Unbalanced Data Processsing
复制标题
一种用于分类不平衡数据处理的混合特征选择算法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Xueni Li
中科院分区：
文献类型：
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作者：
Xue Zhang;Zhiguo Shi;Xuan Liu;Xueni Li
特征选择的结果直接影响分类器的性能和准确率。在一类F-Score特征选择和改进的F-Score特征选择和遗传算法的基础上，结合K近邻、支持向量机、随机森林、朴素贝叶斯等机器学习方法，提出了一种混合特征选择算法来处理两类不平衡数据问题和多类分类问题。与传统的机器学习算法相比，它可以在更广阔的特征空间进行搜索，并促使分类器根据启发式规则来处理不平衡数据集的特征，可以更好地处理不平衡分类问题。实验结果表明，与其他模型相比，该模型对两类分类的接收者工作特征曲线下面积和多类分类问题的准确率均有提高
The performance and accuracy of classifier are affected by the result of feature selection directly. Based on the one-class F-Score feature selection and the improved F-Score feature selection and genetic algorithm, combined with machine learning methods like the K nearest neighbor, support vector machine, random forest, naive Bayes, a hybrid feature selection algorithm is proposed to process the two classification unbalanced data problem and multi classification problem. Compared with the traditional machine learning algorithm, it can search in wider feature space and promote classifier to deal with the characteristics of unbalanced data sets according to heuristic rules, which can handle the problem of unbalanced classification better. The experiment results show that the area under receiver operating characteristic curve for two classifications and the accuracy rate for multi classification problem have been improved compared with other models