Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique
The features related to the real world data may be redundant and erroneous in nature. The vital role of feature selection (FS) in handling such type of features cannot be ignored in the area of computational learning. The two most commonly used objectives for FS are the maximisation of the accuracy...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-10-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2018.1487384 |
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| author | Subrat Kumar Nayak Pravat Kumar Rout Alok Kumar Jagadev Tripti Swarnkar |
| author_facet | Subrat Kumar Nayak Pravat Kumar Rout Alok Kumar Jagadev Tripti Swarnkar |
| author_sort | Subrat Kumar Nayak |
| collection | DOAJ |
| description | The features related to the real world data may be redundant and erroneous in nature. The vital role of feature selection (FS) in handling such type of features cannot be ignored in the area of computational learning. The two most commonly used objectives for FS are the maximisation of the accuracy and minimisation of the number of features. This paper presents an Elitism-based Multi-objective Differential Evolution algorithm for FS and the novelty lies in the searching process which uses Minkowski Score (MS) and simultaneously optimises three objectives. The MS is considered as the third objective to keep track of the feature subset which is capable enough to produce a good classification result even if the average accuracy is poor. Extreme Learning Machine because of its fast learning speed and high efficiency has been considered with this multi-objective approach as a classifier for FS. Twenty-one benchmark datasets have been considered for performance evaluation. Moreover, the selected feature subsets are tested using 10-fold cross-validation. A comparative analysis of the proposed approach with two classical models, three single objective algorithms, and four multi-objective algorithms has been carried out to test the efficacy of the model. |
| format | Article |
| id | doaj-art-13aa56005b3749a6b38d7f9fc34c5d5e |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2018-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-13aa56005b3749a6b38d7f9fc34c5d5e2025-08-20T02:01:42ZengTaylor & Francis GroupConnection Science0954-00911360-04942018-10-0130436238710.1080/09540091.2018.14873841487384Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching techniqueSubrat Kumar Nayak0Pravat Kumar Rout1Alok Kumar Jagadev2Tripti Swarnkar3Siksha “O” Anusandhan (Deemed to be University)Siksha “O” Anusandhan (Deemed to be University)KIIT Deemed to be UniversitySiksha “O” Anusandhan (Deemed to be University)The features related to the real world data may be redundant and erroneous in nature. The vital role of feature selection (FS) in handling such type of features cannot be ignored in the area of computational learning. The two most commonly used objectives for FS are the maximisation of the accuracy and minimisation of the number of features. This paper presents an Elitism-based Multi-objective Differential Evolution algorithm for FS and the novelty lies in the searching process which uses Minkowski Score (MS) and simultaneously optimises three objectives. The MS is considered as the third objective to keep track of the feature subset which is capable enough to produce a good classification result even if the average accuracy is poor. Extreme Learning Machine because of its fast learning speed and high efficiency has been considered with this multi-objective approach as a classifier for FS. Twenty-one benchmark datasets have been considered for performance evaluation. Moreover, the selected feature subsets are tested using 10-fold cross-validation. A comparative analysis of the proposed approach with two classical models, three single objective algorithms, and four multi-objective algorithms has been carried out to test the efficacy of the model.http://dx.doi.org/10.1080/09540091.2018.1487384multi-objectivefeature selectionhypervolumeextreme learning machinedifferential evolution |
| spellingShingle | Subrat Kumar Nayak Pravat Kumar Rout Alok Kumar Jagadev Tripti Swarnkar Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique Connection Science multi-objective feature selection hypervolume extreme learning machine differential evolution |
| title | Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique |
| title_full | Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique |
| title_fullStr | Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique |
| title_full_unstemmed | Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique |
| title_short | Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique |
| title_sort | elitism based multi objective differential evolution with extreme learning machine for feature selection a novel searching technique |
| topic | multi-objective feature selection hypervolume extreme learning machine differential evolution |
| url | http://dx.doi.org/10.1080/09540091.2018.1487384 |
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