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 v...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Hu Guan, Jingyu Zhou, Minyi Guo
• Keywords: Internet Technology, State-of-the-art Svm Classifiers, Text Categorization, Text Categorization Problem, WWW 2009