Name
Abstract | ||
|---|---|---|
We introduce a new method of feature selection for text categorization. Our MMR-based feature selection method strives to reduce redundancy between features while maintaining information gain in selecting appropriate features for text categorization. Empirical results show that MMR-based feature selection is more effective than Koller & Sahami's method, which is one of greedy feature selection methods, and conventional information gain which is commonly used in feature selection for text categorization. Moreover, MMR-based feature selection sometimes produces some improvements of conventional machine learning algorithms over SVM which is known to give the best classification accuracy. |
Year | Venue | Keywords |
|---|---|---|
2004 | HLT-NAACL (Short Papers) | feature selection,mmr-based feature selection method,greedy feature selection method,new method,text categorization,conventional information gain,conventional machine,appropriate feature,mmr-based feature selection,information gain |
DocType | ISBN | Citations |
Conference | 1-932432-24-8 | 3 |
PageRank | References | Authors |
0.38 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changki Lee | 1 | 279 | 26.18 |
| Gary Geunbae Lee | 2 | 932 | 93.23 |