Class-dependent projection based method for text categorization

Lifei Chen

doi:10.1016/j.patrec.2011.01.018

Return

Class-dependent projection based method for text categorization

2011-07-01

PRE

Lifei Chen *

Gongde Guo

Kaijun Wang

DOI：10.1016/j.patrec.2011.01.018

Original

Original request for help

Save

Abstract

En 中文

Text categorization presents unique challenges to traditional classification methods due to the large number of features inherent in the datasets from real-world applications of text categorization, and a great deal of training samples. In high-dimensional document data, the classes are typically categorized only by subsets of features, which are typically different for the classes of different topics. This paper presents a simple but effective classifier for text categorization using class-dependent projection based method. By projecting onto a set of individual subspaces, the samples belonging to different document classes are separated such that they are easily to be classified. This is achieved by developing a new supervised feature weighting algorithm to learn the optimized subspaces for all the document classes. The experiments carried out on common benchmarking corpuses showed that the proposed method achieved both higher classification accuracy and lower computational costs than some distinguishing classifiers in text categorization, especially for datasets including document categories with overlapping topics. (C) 2011 Elsevier B.V. All rights reserved.

Keywords:

Text categorization

Classification

Projection

Class-dependence

Feature weighting

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.