关键词: 特征选择, 最大相关最小冗余, 多准则赋权排序, C-支持向量机, 顺序前向浮动搜索

Abstract: Large-scaled and multi-dimension in data mining which may increase the storage of system, lead to the waste of time and low-accuracy precision. A new feature selection approach mCRC based on multi-criterion ranking and C-SVM is introduced in this paper towards the defects of present feature selection such as low accuracy and undeterminable amounts of optimal features. mCRC computes the dependencies between features and labels through mutual information and class distances, meanwhile deletes irrelevant and completely redundant attributes, and then all features based on dependencies and redundancies are ranked. For the sake of obtaining the optimal subset, it uses C-SVM classifiers to filtrate the sorted features via Sequential Forward Floating Selection（SFFS）. Compared with traditional algorithm, mCRC algorithm achieves high accuracy with less training time, it’s able to provide a strong guarantee for quick and efficient massive data mining.

Key words: feature selection, maximum relevance minimum redundancy, multi-criterion ranking, C-SVM, Sequential Forward Floating Selection（SFFS）