{"title":"探索性二元灰狼优化器与二次插值特征选择。","authors":"Yijie Zhang, Yuhang Cai","doi":"10.3390/biomimetics9100648","DOIUrl":null,"url":null,"abstract":"<p><p>The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. Experimental results, compared with other advanced algorithms, demonstrate the effectiveness of the proposed algorithm.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505495/pdf/","citationCount":"0","resultStr":"{\"title\":\"Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection.\",\"authors\":\"Yijie Zhang, Yuhang Cai\",\"doi\":\"10.3390/biomimetics9100648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. 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引用次数: 0
摘要
大型数据集的高维度会严重影响数据挖掘过程。因此,特征选择成为一个必不可少的预处理阶段,目的是通过选择信息量最大的特征来降低数据集的维度,同时提高分类准确率。本文提出了一种新颖的二元灰狼优化算法来解决分类任务中的特征选择问题。首先,搜索代理的历史最优位置有助于探索更有前景的领域。因此,通过线性组合搜索代理的最佳位置,可以提高算法的探索能力,从而增强其全局开发能力。其次,新颖的二次插值技术将种群多样性与局部开发相结合,有助于提高种群多样性和收敛精度。第三,在探索阶段对收敛因子施加混沌扰动(小的随机波动),有助于避免过早收敛,促进对搜索空间的探索。最后,新颖的转移函数在不同阶段对特征信息进行不同处理,使算法能够在二进制空间中有效搜索和优化,从而选择最佳特征子集。所提出的方法采用了 k 近邻分类器,并通过 32 个数据集的 10 倍交叉验证来评估性能。实验结果与其他先进算法相比,证明了所提算法的有效性。
Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection.
The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. Experimental results, compared with other advanced algorithms, demonstrate the effectiveness of the proposed algorithm.