A New Feature Selection and Feature Contrasting Approach Based on Quality Metric: Application to Efficient Classification of Complex Textual Data
Identifieur interne : 001572 ( Istex/Curation ); précédent : 001571; suivant : 001573A New Feature Selection and Feature Contrasting Approach Based on Quality Metric: Application to Efficient Classification of Complex Textual Data
Auteurs : Jean-Charles Lamirel [France] ; Pascal Cuxac [France] ; Aneesh Sreevallabh Chivukula [Inde] ; Kafil Hajlaoui [France]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Feature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. In this paper we go one step further showing that a straightforward adaptation of such metric can provide a highly efficient feature selection and feature contrasting model in the context of supervised classification. We more especially show that this technique can enhance the performance of classification methods whilst very significantly outperforming (+80%) the state-of-the art feature selection techniques in the case of the classification of unbalanced, highly multidimensional and noisy textual data, with a high degree of similarity between the classes.
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DOI: 10.1007/978-3-642-40319-4_32
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In this paper we go one step further showing that a straightforward adaptation of such metric can provide a highly efficient feature selection and feature contrasting model in the context of supervised classification. We more especially show that this technique can enhance the performance of classification methods whilst very significantly outperforming (+80%) the state-of-the art feature selection techniques in the case of the classification of unbalanced, highly multidimensional and noisy textual data, with a high degree of similarity between the classes.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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