Combination of Evidence-Based Classifiers for Text Categorization

Abstract -- In this paper we propose an evidential fusion approach to combining the decisions of text classifiers. These text classifiers are generated by four widely used learning algorithms: Support Vector Machine (SVM), kNN (Nearest Neighbour), kNN model-based approach (kNNM), and Rocchio on two text corpora. We first model each classifier output as a list of prioritized decisions and then divide it into the subsets of 2 and 3 decisions which are subsequently represented by the evidential structures in terms of triplet and quartet. We also develop the general formulae based on the Dempster- Shafer theory of evidence for combining such decisions. To validate our method various experiments have been carried out over the data sets of 20-newsgroup and Reuters-21578, and a comparative analysis with an alternative dichotomous structure and with majority voting have also been conducted to demonstrate the advantage of our approach in combining text classifiers.