An Improved Method of Feature Selection Based on Concept Attributes in Text Classification

The feature selection and weighting are two important parts of automatic text classification. In this paper we give a new method based on concept attributes. We use the DEF Terms of the Chinese word to extract concept attributes, and a Concept Tree (C-Tree) to give these attributes proper weighs considering their positions in the C-Tree, as this information describe the expression powers of the attributes. If these attributes are too weak to sustain the main meanings of the words, they will be deserted and the original word will be reserved. Otherwise, the attributes are selected in stead of the original words. Our main research purpose is to make a balance between concept features and word ones by set a shielded level as the threshold of the feature selection after weighting these features. According to the experiment results, we conclude that we can get enough information from the combined feature set for classification and efficiently reduce the useless features and the noises. In o...

• Type: Conference
• Year: 2005
• Where: ICNC
• Authors: Shasha Liao, Minghu Jiang
• Keywords: Attributes Proper Weighs, Concept Attributes, Feature Selection, ICNC 2005