关键词: 文本特征, 聚类加权, 最小二乘支持向量机, 布谷鸟搜索算法

Abstract: Text feature extraction and classifier optimization are two key problems for text categorization, in order to improve correct rate of text classification, this paper proposes a text classification model based on Clustering Weighted（CW） and Least Square Support Vector Machine（LSSVM） optimized by the Cuckoo Search（CS） algorithm. TF-IDF algorithm is used to calculate the feature weights, the feature is weighted by words position and features are clustered to reduced feature redundancy, the LSSVM is used to build text classifier which is optimized by CS algorithm. Fudan University data is used to test the performance of the proposed model. The simulation results show that the proposed model not only improves the classification accuracy, but also improves the efficiency of text classification.

Key words: text feature, clustering weighted, least square support vector machine, cuckoo search algorithm