IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jiahao Li , Tao Luo, Baitao Zhang, Min Chen, Jie Zhou
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引用次数: 0

Abstract

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.
IMOABC:用于高维特征选择的高效多目标滤波器-包装器混合方法
随着数据科学的发展,高维数据的挑战变得越来越普遍。高维数据包含大量冗余信息,会对机器学习算法的性能和效果产生不利影响。因此,有必要从原始数据中选择最相关的特征,并对高维数据进行特征选择。本文提出了一种基于改进的多目标人工蜂群算法(IMOABC)的新型滤波包特征选择方法,以解决高维数据中的特征选择问题。该方法同时考虑了特征误差率、特征子集比和距离三个目标,有效提高了在高维数据中获得最佳特征子集的效率。此外,该方法还引入了一种基于 Fisher Score 的新型初始化策略,大大提高了解决方案的质量。此外,还设计了一种新的动态自适应策略,有效提高了算法的收敛速度,增强了全局搜索能力。微阵列癌症数据集的对比实验结果表明,与各种传统和最先进的多目标特征选择算法相比,IMOABC 能显著提高分类准确率,并有效减少特征子集的大小。与各种多目标特征选择方法相比,IMOABC 的分类准确率平均提高了 2.27%,而所选特征的数量平均减少了 88.76%。同时,与各种传统方法相比,IMOABC 在所有数据集上的分类准确率平均提高了 4.24%,所选特征的数量平均减少了 76.73%。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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