Document classification using Symbolic classifiers

In this paper, we present symbolic classifiers to classify text documents. We propose to use cluster based symbolic representation followed by symbolic feature selection methods to classify text documents. In particular, we propose Symbolic clustering approaches; Symbolic cluster based without feature selection; Symbolic cluster based with feature selection (using similarity measure); Symbolic cluster based with feature selection (using dissimilarity measure) and Symbolic feature clustering approaches. The above mentioned representation methods are very powerful in reducing the dimensionality of feature vectors for text classification. To corroborate the efficacy of the proposed model, we conducted extensive experimentation on various standard text datasets. The experimental results reveal that the symbolic feature clustering approach achieves better classification accuracy over the existing cluster based symbolic approaches.