Multiple Feature-Classifier Combination in Automated Text Classification

Automatic text classification (ATC) is important in applications such as indexing and organizing electronic documents in databases leading to enhancement of information access and retrieval. We propose a method which employs various types of feature sets and learning algorithms to improve classification effectiveness. Unlike the conventional methods of multi-classifier combination, the proposed method considers the contributions of various types of feature sets and classifiers. It can therefore be known as multiple feature-classifier combination (MFC) method. In this paper we present empirical evaluation of MFC using two benchmarks of text collections to determine its effectiveness. Empirical evaluation show that MFC consistently outperformed all compared methods.