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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li-Ju Gao Been-Chian Chien |
| Copyright Year | 2012 |
| Abstract | Text classification is an important research topic for managing numerous electronic documents. Feature reduction is the key issue for text classification with high dimensional keywords. A document analysis method called discriminant coefficient was proposed to reduce features and achieve high precisiontext classification. However, the main problem of the discriminant based feature reduction method is that the final number of reduced features is exactly equal to the number of document classes. Although the precisions of classification are high in such a method, the recalls are relatively low. In this paper, we propose an improvement on the analyzing method indiscriminant coefficients. We apply a simple clustering method to distinguish the documents in each document class to reserve hidden differences among keywords in the same class. The clustering results can help to adjust the number of reduction features flexibly. The experimental results show that the proposed clustering mechanism supports adaptive features reduction and both of the recall and F1 measurements are improved. |
| Starting Page | 137 |
| Ending Page | 142 |
| File Size | 370286 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467349765 |
| DOI | 10.1109/TAAI.2012.16 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-16 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Discriminant coefficient Clustering methods Text categorization Clustering algorithms Classification Feature extraction Classification algorithms Feature clustering Matrix converters Feature reduction |
| Content Type | Text |
| Resource Type | Article |
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