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An extensive empirical study of feature selection metrics for text classification (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Forman, George |
| Description | supervised machine learning, document categorization, support vector machines, information gain, binormal Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g. Information Gain) evaluated on a benchmark of 229 text classification pr oblem instances that |
| File Format | |
| Language | English |
| Publisher Date | 2008-01-01 |
| Publisher Institution | In CIKM ’08: Proceeding of the 17th ACM conference on Information and knowledge mining |
| Access Restriction | Open |
| Subject Keyword | Extensive Empirical Study Feature Selection Metric Support Vector Machine Document Routing Twelve Feature Selection Method Machine Learning Text Classification Pr Oblem Instance Binormal Machine Learning News Filtering Text Classification Learning Task Efficient Text Domain Document Categorization Effective Feature Selection Information Gain Empirical Comparison |
| Content Type | Text |
| Resource Type | Article |
