document: A class-feature-centroid classifier for text categorization 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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