TY  - CONF
ID  - www200921
UR  - http://www2009.eprints.org/21/
A1  - Guan, Hu
A1  - Zhou, Jingyu
A1  - Guo, Minyi
Y1  - 2009/04//
N2  - Automated text categorization is an important technique for many web applications, such as document indexing, document ?ltering, and cataloging web resources. Many different approaches have been proposed for the automated text categorization problem. Among them, centroid-based approaches have the advantages of short training time and testing time due to its computational efficiency. As a result, centroid-based classi?ers have been widely used in many web applications. However, the accuracy of centroid-based classi?ers is inferior to SVM, mainly because centroids found during construction are far from perfect locations. We design a fast Class-Feature-Centroid (CFC) classi?er for multi-class, single-label text categorization. In CFC, a centroid is built from two important class distributions: inter-class term index and inner-class term index. CFC proposes a novel combination of these indices and employs a denormalized cosine measure to calculate the similarity score between a text vector and a centroid. Experiments on the Reuters-21578 corpus and 20-newsgroup email collection show that CFC consistently outperforms the state-of-the-art SVM classi?ers on both micro-F1 and macro-F1 scores. Particularly, CFC is more effective and robust than SVM when data is sparse.
TI  - A Class-Feature-Centroid Classifier for Text Categorization
SP  - 201
M2  - Madrid, Spain
AV  - public
EP  - 201
T2  - 18th International World Wide Web Conference
ER  -