A Hybrid Feature Selection Method Based on Rough Conditional Mutual Information and Naive Bayesian Classifier
| Autor: | Rui Zhang, Youliang Zhang, Zilin Zeng, Hong-Jun Zhang |
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| Rok vydání: | 2014 |
| Předmět: |
Article Subject
Naive bayesian classifier business.industry Computer science Conditional mutual information Pattern recognition Feature selection Filter (signal processing) computer.software_genre Measure (mathematics) Feature (computer vision) Metric (mathematics) Artificial intelligence Rough set Data mining business computer |
| Zdroj: | ISRN Applied Mathematics. 2014:1-11 |
| ISSN: | 2090-5572 |
| Popis: | We introduced a novel hybrid feature selection method based on rough conditional mutual information and Naive Bayesian classifier. Conditional mutual information is an important metric in feature selection, but it is hard to compute. We introduce a new measure called rough conditional mutual information which is based on rough sets; it is shown that the new measure can substitute Shannon’s conditional mutual information. Thus rough conditional mutual information can also be used to filter the irrelevant and redundant features. Subsequently, to reduce the feature and improve classification accuracy, a wrapper approach based on naive Bayesian classifier is used to search the optimal feature subset in the space of a candidate feature subset which is selected by filter model. Finally, the proposed algorithms are tested on several UCI datasets compared with other classical feature selection methods. The results show that our approach obtains not only high classification accuracy, but also the least number of selected features. |
| Databáze: | OpenAIRE |
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