Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE.
翻译:最近,为解决特征选择问题,制定了许多渐进式计算方法;然而,这些研究主要侧重于小规模问题,导致当地选择的停滞问题,在处理大规模特征选择难题时,造成数字不稳定。为了应对这些挑战,本文件提议了一种基于自我适应机制的新型加权差异演化算法,称为SAWDE,以解决大规模特征选择问题。首先,采用了一个多人口机制,以加强人口的多样性。然后,我们提议了一个新的自我适应机制,从一个战略集合中选择若干战略战略,从历史信息中获取数据集的不同特征。最后,设计了一个加权模型,以确定重要特征,使我们的模型能够产生最合适的特征选择解决方案。我们展示了我们在12个大规模数据集上的算法的有效性。SWDE的业绩优于6种非欧盟委员会的算法和另外6种欧盟委员会的算法,既包括培训和测试数据集,也包括测试大型特征选择问题的匹配工具。此外,我们将SAWDE的12级算法与SAWDS-S-S-SW-S-SD-SD-SD-SD-SD-SV-SVAVAs squal-Syal-SWD-SD-SD-SD-SVD-SD-SD-SD-SD-SD-SD-SVD-SVD-SVD-SVD-SD-SD-SD-SD-SD-SVD-SD-SD-SVD-SD-SD-SD-SD-SD-SD-SD-SD-SVD-SVD-SVD-SVD-SVD-SD-SD-S-S-SD-S-S-SD-SD-S-S-SD-SD-SD-SD-S-S-S-S-S-SD-SD-SD-S-S-SD-SD-S-S-SVD-S-S-SV-S-S-S-S-S-S-S-S-S-S-SD-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S