Feature selecting model in automatic text categorization of Chinese financial industrial news

Authors:
Huey-Ming Lee;Pin-Jen Chen;Tsung-Yen Lee
Affiliations:
Department of Information Management, Chinese Culture University, Taipei, Taiwan;Department of Information Management, Chinese Culture University, Taipei, Taiwan;Department of Securities, Bank of Overseas Chinese, Taiwan
Venue:
ICC'06 Proceedings of the 10th WSEAS international conference on Circuits
Year:
2006

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Abstract

This work focuses on selecting features in the automatic text categorization of Chinese industrial and financial news. We use feature selecting method for the characteristics of subclass Chinese financial and industrial news. However, it is an open challenge for subclass news in solving real-world problems which are often high-dimensional. Therefore, we proposed a feature selecting model in automatic text categorization of Chinese financial industrial news. This model can not only discover features from training news, but also can tune features through testing news. The proposed model help to classify subclass news, and it will be useful to knowledge management. Furthermore, feature selection has received considerable attention in improving the performance of the classification.