Ensemble Learning Based Feature Selection with an Application to Text Classification
| dc.contributor.author | Onan, A | |
| dc.date.accessioned | 2024-07-18T11:46:47Z | |
| dc.date.available | 2024-07-18T11:46:47Z | |
| dc.description.abstract | An important problem of text classification is high dimensionality. The performance of different feature selection methods can change based on the characteristics of different datasets. In this study, a feature selection method is developed, which integrates different filter-based feature selection methods by an ensemble learning approach. In the presented method, feature rankings obtained by five filter-based feature selection methods (mutual information measure, chi-square statistics, odds ratio, information gain and weighted log likelihood ratio) are aggregated by enhanced Borda count rank aggregation. In the experimental analysis, Reuters-21578 and 20 Newsgroups datasets are employed on support vector machines and C4.5 classifier. The experimental results indicate that the presented method outperforms conventional filter-based feature selection schemes. | |
| dc.identifier.issn | 2165-0608 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/3012 | |
| dc.language.iso | Turkish | |
| dc.publisher | IEEE | |
| dc.subject | SCHEME | |
| dc.title | Ensemble Learning Based Feature Selection with an Application to Text Classification | |
| dc.type | Proceedings Paper |