Efficient text classification using best feature selection and combination of methods

Abstract

Lsquare and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. 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 propose voting method and OWA operator and Decision Template method for combining classifiers. In these we use an effective and efficient new method called variance-mean based feature filtering method of feature selection. Best feature selection method and combination of methods are used to do feature reduction in the representation phase of text classification is proposed. Using this efficient feature selection method and best classifier combination method we improve the text classification performance. © 2009 Springer Berlin Heidelberg.