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

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A class-feature-centroid classifier for text categorization

Content Provider	ACM Digital Library
Author	Guo, Minyi Guan, Hu Zhou, Jingyu
Abstract	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.
Starting Page	201
Ending Page	210
Page Count	10
File Format	PDF
ISBN	9781605584874
DOI	10.1145/1526709.1526737
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-04-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Centroid Inter-class Denormalized cosine measure Inner-class Text classification
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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