Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method
| Autor: | N. R. Sunitha, Sajal Raj Joshi, S. Sitharama Iyengar, G. S. Thejas, Prajwal Badrinath |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science business.industry filter method General Engineering Pattern recognition Feature selection hybrid feature selection 02 engineering and technology Normalized mutual information mini batch K-means Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business normalized mutual information Classifier (UML) lcsh:TK1-9971 random forest |
| Zdroj: | IEEE Access, Vol 7, Pp 116875-116885 (2019) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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