دورية أكاديمية
A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO.
| العنوان: | A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO. |
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| المؤلفون: | Wang YY; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, China., Zhang H; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, China., Qiu CH; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, China., Xia SR; Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, China. |
| المصدر: | Computational intelligence and neuroscience [Comput Intell Neurosci] 2018 May 06; Vol. 2018, pp. 5078268. Date of Electronic Publication: 2018 May 06 (Print Publication: 2018). |
| نوع المنشور: | Evaluation Study; Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Hindawi Pub. Corp Country of Publication: United States NLM ID: 101279357 Publication Model: eCollection Cited Medium: Internet ISSN: 1687-5273 (Electronic) NLM ISO Abbreviation: Comput Intell Neurosci Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York, NY : Hindawi Pub. Corp. |
| مواضيع طبية MeSH: | Machine Learning* |
| مستخلص: | The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets. The proposed method obtains six lowest MSE values among all the comparative methods. Experimental results demonstrate that the proposed method has the superiority of getting lower MSE with the same scale of feature subset or requiring smaller scale of feature subset for similar MSE. |
| References: | IEEE Trans Cybern. 2013 Dec;43(6):1656-71. (PMID: 24273143) IEEE Trans Neural Netw Learn Syst. 2017 Apr;28(4):1005-1010. (PMID: 26863673) IEEE Trans Neural Netw. 1998;9(1):224-9. (PMID: 18252445) IEEE Trans Cybern. 2017 Jun;47(6):1496-1509. (PMID: 28113688) IEEE/ACM Trans Comput Biol Bioinform. 2011 Mar-Apr;8(2):452-63. (PMID: 21233525) Comput Math Methods Med. 2017;2017:9512741. (PMID: 28246543) |
| تواريخ الأحداث: | Date Created: 20180602 Date Completed: 20180920 Latest Revision: 20191210 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC5960553 |
| DOI: | 10.1155/2018/5078268 |
| PMID: | 29853832 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1687-5273 |
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| DOI: | 10.1155/2018/5078268 |