General framework for class-specific feature selection
| Autor: | Jesús Ariel Carrasco-Ochoa, J. Fco. Martínez-Trinidad, Bárbara B. Pineda-Bautista |
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| Rok vydání: | 2011 |
| Předmět: |
Computer science
business.industry Dimensionality reduction General Engineering Feature selection Pattern recognition Machine learning computer.software_genre Computer Science Applications Artificial Intelligence Feature (computer vision) Minimum redundancy feature selection Artificial intelligence business computer Classifier (UML) |
| Zdroj: | Expert Systems with Applications. 38:10018-10024 |
| ISSN: | 0957-4174 |
| Popis: | Commonly, when a feature selection algorithm is applied, a single feature subset is selected for all the classes, but this subset could be inadequate for some classes. Class-specific feature selection allows selecting a possible different feature subset for each class. However, all the class-specific feature selection algorithms have been proposed for a particular classifier, which reduce their applicability. In this paper, a general framework for using any traditional feature selector for doing class-specific feature selection, which allows using any classifier, is proposed. Experimental results and a comparison against traditional feature selectors showing the suitability of the proposed framework are included. |
| Databáze: | OpenAIRE |
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