Text Classification Model Based on Multiconlitron

To deal with the problem of text classification, a text categorization method was proposed based on multiconlitron from the perspective of piecewise learning. First,text sample preprocessing including feature selection and feature weighting was performed. Then, the multiconlitron was constructed by using growing support multiconlitron algorithm (GSMA) and support multiconlitron algorithm (SMA) respectively for text classification. Inspired by the idea of maximum interval of support vector machine, the classification of two kinds of data by integrating the linear classifier was achieved by this model, which had the advantages of small computation cost and strong adaptive ability. Experiments on standard text data sets show that the proposed method has a good performance on text classification and the comparison results with some other typical text classification methods also verifies the effectiveness of the proposed method.