Naive Bayes-Guided Bat Algorithm for Feature Selection
When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/325973 |
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| _version_ | 1832554491133558784 |
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| author | Ahmed Majid Taha Aida Mustapha Soong-Der Chen |
| author_facet | Ahmed Majid Taha Aida Mustapha Soong-Der Chen |
| author_sort | Ahmed Majid Taha |
| collection | DOAJ |
| description | When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. |
| format | Article |
| id | doaj-art-5328c7b49f0c44db9acc7c74c7fbb2e7 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-5328c7b49f0c44db9acc7c74c7fbb2e72025-02-03T05:51:21ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/325973325973Naive Bayes-Guided Bat Algorithm for Feature SelectionAhmed Majid Taha0Aida Mustapha1Soong-Der Chen2College of Graduate Studies, Universiti Tenaga Nasional, 43000 Kajang, Selangor, MalaysiaFaculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, MalaysiaCollege of Information Technology, Universiti Tenaga Nasional, 43000 Kajang, Selangor, MalaysiaWhen the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.http://dx.doi.org/10.1155/2013/325973 |
| spellingShingle | Ahmed Majid Taha Aida Mustapha Soong-Der Chen Naive Bayes-Guided Bat Algorithm for Feature Selection The Scientific World Journal |
| title | Naive Bayes-Guided Bat Algorithm for Feature Selection |
| title_full | Naive Bayes-Guided Bat Algorithm for Feature Selection |
| title_fullStr | Naive Bayes-Guided Bat Algorithm for Feature Selection |
| title_full_unstemmed | Naive Bayes-Guided Bat Algorithm for Feature Selection |
| title_short | Naive Bayes-Guided Bat Algorithm for Feature Selection |
| title_sort | naive bayes guided bat algorithm for feature selection |
| url | http://dx.doi.org/10.1155/2013/325973 |
| work_keys_str_mv | AT ahmedmajidtaha naivebayesguidedbatalgorithmforfeatureselection AT aidamustapha naivebayesguidedbatalgorithmforfeatureselection AT soongderchen naivebayesguidedbatalgorithmforfeatureselection |