This item is a Paper in the Data Mining track.
Automated text categorization is an important technique for many web applications, such as document indexing, document ﬁltering, 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 classiﬁers have been widely used in many web applications. However, the accuracy of centroid-based classiﬁers is inferior to SVM, mainly because centroids found during construction are far from perfect locations. We design a fast Class-Feature-Centroid (CFC) classiﬁer 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 classiﬁers on both micro-F1 and macro-F1 scores. Particularly, CFC is more effective and robust than SVM when data is sparse.
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