Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method
Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to incre...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8805315/ |
| _version_ | 1818412631705255936 |
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| author | G. S. Thejas Sajal Raj Joshi S. S. Iyengar N. R. Sunitha Prajwal Badrinath |
| author_facet | G. S. Thejas Sajal Raj Joshi S. S. Iyengar N. R. Sunitha Prajwal Badrinath |
| author_sort | G. S. Thejas |
| collection | DOAJ |
| description | Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to increase the efficacy of the predictor by finding the optimal set of features. The feature subset should be such that it enhances the classification accuracy by the removal of redundant features. We propose a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods. Our hybrid model is based on a two phase process where we rank the features and then choose the best subset of features based on the ranking. We validated our model with various datasets, using multiple evaluation metrics. Furthermore, we have also compared and analyzed our results with previous works. The proposed model outperformed many existent algorithms and has given us good results. |
| first_indexed | 2024-12-14T10:50:23Z |
| format | Article |
| id | doaj.art-766b667e61104e6c8500828434e9c06e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:50:23Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-766b667e61104e6c8500828434e9c06e2022-12-21T23:05:16ZengIEEEIEEE Access2169-35362019-01-01711687511688510.1109/ACCESS.2019.29363468805315Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection MethodG. S. Thejas0https://orcid.org/0000-0001-9606-0128Sajal Raj Joshi1S. S. Iyengar2N. R. Sunitha3Prajwal Badrinath4School of Computing and Information Sciences, Florida International University, Miami, FL, USADepartment of Computer Science Engineering, Siddaganga Institute of Technology, Tumakuru, IndiaSchool of Computing and Information Sciences, Florida International University, Miami, FL, USADepartment of Computer Science Engineering, Siddaganga Institute of Technology, Tumakuru, IndiaSchool of Computing and Information Sciences, Florida International University, Miami, FL, USAFeature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to increase the efficacy of the predictor by finding the optimal set of features. The feature subset should be such that it enhances the classification accuracy by the removal of redundant features. We propose a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods. Our hybrid model is based on a two phase process where we rank the features and then choose the best subset of features based on the ranking. We validated our model with various datasets, using multiple evaluation metrics. Furthermore, we have also compared and analyzed our results with previous works. The proposed model outperformed many existent algorithms and has given us good results.https://ieeexplore.ieee.org/document/8805315/Feature selectionfilter methodhybrid feature selectionnormalized mutual informationmini batch K-meansrandom forest |
| spellingShingle | G. S. Thejas Sajal Raj Joshi S. S. Iyengar N. R. Sunitha Prajwal Badrinath Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method IEEE Access Feature selection filter method hybrid feature selection normalized mutual information mini batch K-means random forest |
| title | Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method |
| title_full | Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method |
| title_fullStr | Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method |
| title_full_unstemmed | Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method |
| title_short | Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method |
| title_sort | mini batch normalized mutual information a hybrid feature selection method |
| topic | Feature selection filter method hybrid feature selection normalized mutual information mini batch K-means random forest |
| url | https://ieeexplore.ieee.org/document/8805315/ |
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