Text classification using combined sparse representation classifiers and support vector machines

摘要

Text classification is an important task in managing huge repository of textual content prevailing in various domains. In this paper, we propose to use sparse representation classifier (SRC) and support vector machines (SVMs) based classifiers using frequency-based kernels for text classification. We consider term-frequency (TF) representation for a text document. The sparse representation of an example is obtained by using an overcomplete dictionary made up of TF vectors corresponding to all the training documents [1]. We propose to seed the dictionary using principal components of TF vector representation corresponding to training text documents. SVM-based text classifiers use linear kernel or Gaussian kernel on the TF vector representation of documents. TF representation being a non-negative, histogram representation, we propose to build SVM-based text classifiers using frequency-based kernels such as histogram intersection kernel, Chi-square (X2) kernel and Hellinger's kernel. It is observed that the examples misclassified by one classifier is correctly classified in another classifier. To take advantage of the various classifiers, we introduce an approach to combine classifiers to improve the performance of text classification. The effectiveness of all the proposed techniques for text classification is demonstrated on 20 Newsgroup Corpus.