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- W2390164785 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." @default.
- W2390164785 created "2016-06-24" @default.
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- W2390164785 date "2013-01-01" @default.
- W2390164785 modified "2023-09-23" @default.
- W2390164785 title "Text categorization model based on clustering weighted and least square support vector machine optimized by cuckoo search algorithm" @default.
- W2390164785 hasPublicationYear "2013" @default.
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