Feature Selection Methods for Extreme Learning Machines
Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own practical f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/11/9/444 |
| _version_ | 1827663573825880064 |
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| author | Yanlin Fu Qing Wu Ke Liu Haotian Gao |
| author_facet | Yanlin Fu Qing Wu Ke Liu Haotian Gao |
| author_sort | Yanlin Fu |
| collection | DOAJ |
| description | Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own practical feature selection method for their special mechanism. In this study, we proposed a feature selection method for the ELM, named FELM. The proposed algorithm achieves highly efficient dimensionality reduction due to the feature ranking strategy. The FELM can simultaneously complete the feature selection and classification processes. In addition, by incorporating a memorization–generalization kernel into the FELM, the nonlinear case of it is issued (called FKELM). The FKELM can achieve high classification accuracy and extensive generalization by applying the property of memorization of training data. According to the experimental results on different artificial and benchmark datasets, the proposed algorithms achieve significantly better classification accuracy and faster training than the other methods. |
| first_indexed | 2024-03-10T00:44:41Z |
| format | Article |
| id | doaj.art-f81d73f2651a45af849f7a40b86f5182 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T00:44:41Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-f81d73f2651a45af849f7a40b86f51822023-11-23T15:02:07ZengMDPI AGAxioms2075-16802022-08-0111944410.3390/axioms11090444Feature Selection Methods for Extreme Learning MachinesYanlin Fu0Qing Wu1Ke Liu2Haotian Gao3School of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaSchool of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaSchool of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaNo. 92677 Troops of PLA, Qingdao 266100, ChinaExtreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs. However, ELMs still do not have their own practical feature selection method for their special mechanism. In this study, we proposed a feature selection method for the ELM, named FELM. The proposed algorithm achieves highly efficient dimensionality reduction due to the feature ranking strategy. The FELM can simultaneously complete the feature selection and classification processes. In addition, by incorporating a memorization–generalization kernel into the FELM, the nonlinear case of it is issued (called FKELM). The FKELM can achieve high classification accuracy and extensive generalization by applying the property of memorization of training data. According to the experimental results on different artificial and benchmark datasets, the proposed algorithms achieve significantly better classification accuracy and faster training than the other methods.https://www.mdpi.com/2075-1680/11/9/444feature selectionextreme learning machinememorization–generalization kernel |
| spellingShingle | Yanlin Fu Qing Wu Ke Liu Haotian Gao Feature Selection Methods for Extreme Learning Machines Axioms feature selection extreme learning machine memorization–generalization kernel |
| title | Feature Selection Methods for Extreme Learning Machines |
| title_full | Feature Selection Methods for Extreme Learning Machines |
| title_fullStr | Feature Selection Methods for Extreme Learning Machines |
| title_full_unstemmed | Feature Selection Methods for Extreme Learning Machines |
| title_short | Feature Selection Methods for Extreme Learning Machines |
| title_sort | feature selection methods for extreme learning machines |
| topic | feature selection extreme learning machine memorization–generalization kernel |
| url | https://www.mdpi.com/2075-1680/11/9/444 |
| work_keys_str_mv | AT yanlinfu featureselectionmethodsforextremelearningmachines AT qingwu featureselectionmethodsforextremelearningmachines AT keliu featureselectionmethodsforextremelearningmachines AT haotiangao featureselectionmethodsforextremelearningmachines |