By Topic

Multiple Feature-Classifier Combination in Automated Text Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Lazaro S. P. Busagala ; Sokoine Nat. Agric. Libr., Sokoine Univ. of Agric., Morogoro, Tanzania ; Wataru Ohyama ; Tetsushi Wakabayashi ; Fumitaka Kimura

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.

Published in:

Document Analysis Systems (DAS), 2012 10th IAPR International Workshop on

Date of Conference:

27-29 March 2012