Improve text classification accuracy based on classifier fusion methods

Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training data should be provided for training the system. In this approach, documents are represented as vectors where each component is associated with a particular word. We proposed voting methods and OWA operator and decision template method for combining classifiers. Experimental results show that these methods decrese the classification error 15 percent as measured on 2000 training data from 20 newsgroups dataset.