A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers
Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to spe...
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| Format: | Article |
| Language: | English |
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Shahid Rajaee Teacher Training University
2020-01-01
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| Series: | Journal of Electrical and Computer Engineering Innovations |
| Subjects: | |
| Online Access: | https://jecei.sru.ac.ir/article_1443_ce485b250b9343326a2632ae32a316a2.pdf |
| _version_ | 1811306017450885120 |
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| author | Zeinab Khatoun Pourtaheri |
| author_facet | Zeinab Khatoun Pourtaheri |
| author_sort | Zeinab Khatoun Pourtaheri |
| collection | DOAJ |
| description | Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert software programs are utilized to achieve the stability goals of this research.Results: The proposed approach is useful as a preprocessing method before employing heuristic ensemble classifiers; i.e., first discover optimal levels of important parameters and then apply these parameters to heuristic ensemble classifiers to attain the best results. Another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the Pareto front is used as response variable.Finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method.Conclusion: This approach can analyze more parameters with less computational costs in comparison with previous works. This capability is one of the advantages of the proposed method. |
| first_indexed | 2024-04-13T08:36:48Z |
| format | Article |
| id | doaj.art-81b75ba216e64638921e0502d60fb38d |
| institution | Directory Open Access Journal |
| issn | 2322-3952 2345-3044 |
| language | English |
| last_indexed | 2024-04-13T08:36:48Z |
| publishDate | 2020-01-01 |
| publisher | Shahid Rajaee Teacher Training University |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering Innovations |
| spelling | doaj.art-81b75ba216e64638921e0502d60fb38d2022-12-22T02:54:03ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442020-01-018112513410.22061/jecei.2020.6581.3251443A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble ClassifiersZeinab Khatoun Pourtaheri0Mechanical Engineering Department, Higher Education Complex of Bam, Bam, Iran.Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert software programs are utilized to achieve the stability goals of this research.Results: The proposed approach is useful as a preprocessing method before employing heuristic ensemble classifiers; i.e., first discover optimal levels of important parameters and then apply these parameters to heuristic ensemble classifiers to attain the best results. Another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the Pareto front is used as response variable.Finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method.Conclusion: This approach can analyze more parameters with less computational costs in comparison with previous works. This capability is one of the advantages of the proposed method.https://jecei.sru.ac.ir/article_1443_ce485b250b9343326a2632ae32a316a2.pdfensemble classifierheuristic algorithmsmulti-objective inclined planesoptimization algorithmoptimal levelstability |
| spellingShingle | Zeinab Khatoun Pourtaheri A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers Journal of Electrical and Computer Engineering Innovations ensemble classifier heuristic algorithms multi-objective inclined planes optimization algorithm optimal level stability |
| title | A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers |
| title_full | A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers |
| title_fullStr | A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers |
| title_full_unstemmed | A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers |
| title_short | A Preprocessing Technique to Investigate the Stability of Multi-Objective Heuristic Ensemble Classifiers |
| title_sort | preprocessing technique to investigate the stability of multi objective heuristic ensemble classifiers |
| topic | ensemble classifier heuristic algorithms multi-objective inclined planes optimization algorithm optimal level stability |
| url | https://jecei.sru.ac.ir/article_1443_ce485b250b9343326a2632ae32a316a2.pdf |
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