A discriminative and semantic feature selection method for text categorization
| Autor: | Lap Keung Chu, Feng Wu, Wei Zong, D. Sculli |
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| Rok vydání: | 2015 |
| Předmět: |
Economics and Econometrics
business.industry Computer science Semantic feature Big data Pattern recognition Feature selection Management Science and Operations Research Boosting methods for object categorization General Business Management and Accounting Industrial and Manufacturing Engineering Support vector machine ComputingMethodologies_PATTERNRECOGNITION Discriminative model Semantic similarity Feature (computer vision) Artificial intelligence business Classifier (UML) |
| Zdroj: | International Journal of Production Economics. 165:215-222 |
| ISSN: | 0925-5273 |
| DOI: | 10.1016/j.ijpe.2014.12.035 |
| Popis: | Text categorization is an important and critical task in the current era of high volume data storage and handling. Feature selection is obviously one of the most important steps in text categorization. Traditional feature selection methods tend to only consider the correlation between features and categories, and have in the main ignored the semantic similarity between features and documents. To further explore this issue, this paper proposes a novel feature selection method that first selects features in documents with discriminative power and then computes the semantic similarity between features and documents. The proposed feature selection method is tested using a support vector machine (SVM) classifier upon two published datasets, viz. Reuters-21578 and 20-Newsgroups. The experimental results show that the proposed feature selection method generally outperforms the traditional feature selection methods for text categorization for both published datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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