Affiliations: [a] School of Information, Central University of Finance and Economics, Beijing, China
| [b] School of Science and Engineering, Tianjin University of Finance and Economics, Tianjin, China
Correspondence:
[*]
Corresponding author. Youwei Wang, School of Information, Central University of Finance and Economics, Beijing
100081, China. E-mail: ywwang15@126.com.
Abstract: In text classification field, many classifiers cannot deal with the features with large dimensions, thus it is very important to filter the redundant information from the original feature space efficiently and achieve the features with best qualities. On this basis, a new two-step based feature selection method is proposed in this paper. Firstly, some definitions (word semantic correlation, set semantic correlation, semantic correlative and semantic correlative set) are introduced, and the algorithm of generating the semantic correlative sets is given. Secondly, the process of the two-step based feature selection method is described: in the first step, a feature subset is obtained by using an optimal feature selection method, and a set of semantic correlative sets is generated by using the selected feature subset; in the second step, the redundant information of the selected features is filtered by using the generated semantic correlative sets. Finally, in order to avoid local optimum when searching the best threshold, the conception of memory recall position is introduced and an improved memory recall mechanism based fruit fly optimization algorithm is proposed. In the experiments, two typical classifiers: support vector machine and naïve bayes are used on four datasets: Reuters50, SMSSPAS, WebKB and 20-Newsgroups, and the 10-cross validation is carried out when the measurements of F1 and receiver operating curve are used. Experimental results show that the proposed method achieves higher accuracy than several representative traditional feature selection methods and runs faster than typical mutual information based feature selection methods, illustrating its effectiveness on achieving the best features in text classification filed.
Keywords: Feature selection, redundant information, feature space, fruit fly optimization algorithm, support vector machine, receiver operating curve
