Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization
| العنوان: | Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization |
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| المؤلفون: | Qianjin Wei, Chengxian Wang, Yimin Wen |
| المصدر: | Journal of Intelligent & Fuzzy Systems. 40:12023-12038 |
| بيانات النشر: | IOS Press, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Statistics and Probability, 0209 industrial biotechnology, biology, business.industry, Computer science, Extraction (chemistry), General Engineering, Pattern recognition, 02 engineering and technology, biology.organism_classification, Reduction (complexity), 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Social spider |
| الوصف: | Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR. |
| تدمد: | 1875-8967 1064-1246 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::bdd27d1dfd9182bb44fa6e59ded952f3 https://doi.org/10.3233/jifs-210133 |
| رقم الأكسشن: | edsair.doi...........bdd27d1dfd9182bb44fa6e59ded952f3 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18758967 10641246 |
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