Text Categorization Comparison between Simple BPNN and Combinatorial Method of LSI and BPNN

Abstract

This paper proposed a text categorization comparison between simple BPNN and Combinatorial method of LSI and BPNN. In the traditional error back propagation network, the weight adjustment process gets block in a local minima and also the training speed of such network is very slow which leads to reduced performance and reduced efficiency of the network. Also the Learning time of overall network is very high.Hence, to improve the categorization accuracy, a new combinatorial method of LSI (latent semantic Indexing) and BPNN (back propagation neural network) is proposed. The latent semantics demonstration is an accurate data structure in low-dimensional space in which documents, terms and queries are rooted and also compared. Singular value decomposition (SVD) technique is used in Latent semantic Analysis in which large term-document matrix is decomposed into a set of k orthogonal factors by which the original textual data is changed to a smaller semantic space. New document vectors are found in reduced k-dimensional space. Also new coordinates of the queries are found. Here we implement combinatorial method of LSI and BPNN based technique for the classification of 20Newsgroup dataset which include categories of Sports, CS, and Medicine. The proposed technique implemented is compared with the existing BPNN technique. . Hence, this new method greatly reduces the dimension and better classification results can be achieved.

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“…BPNN [30]: All parameters are initialized with randomly assigned values. The dropout rate is set to 0.1 to avoid over-fitting.…”