关键词: 相关性, 冗余度, 稀疏程度, 坐标系

Abstract: In text classification, there are many indexes to evaluate whether feature is good or bad. It mainly concludes relevance of features and classes, features’ redundancy and the sparse degree of features in corpus. It’s necessary to consider full range of features’ various factors because of text features directly affecting the classification result. Feature selection always measures features in three steps respectively: relevance, redundancy and sparse, and every step costs lots of time. The method of step by step is very difficult to use when facing the situation of high real-time and accuracy. To solve these problems, firstly, this paper builds coordinate model and puts relevance, redundancy and sparse into the coordinate. Then, it weights and selects text features according to the angle between vector formed by the point and origin and coordinate plane or axis, thereby, it can integrate many indexes into one index to save time and improve the efficiency of feature selection. The experimental results in Fudan University Chinese corpus and Wangyi text corpus show that compared with the method of step by step, evaluation and selection algorithm based on text features multiple indicator fusion can select word features and n-grams features more quickly and more accurately, and the validity has been validated in SVM.

Key words: relevance, redundancy, sparse, coordinate