A feedback matrix based evolutionary multitasking algorithm for high-dimensional ROC convex hull maximization

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-22 DOI:10.1016/j.ins.2024.121572
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Abstract

Multi-objective evolutionary algorithms have shown their competitiveness in solving ROC convex hull maximization. However, due to “the curse of dimensionality”, few of them focus on high-dimensional ROCCH maximization. Therefore, in this paper, a feedback matrix (FM)-based evolutionary multitasking algorithm, termed as FM-EMTA, is proposed. In FM-EMTA, to tackle “the curse of dimensionality”, a feature importance based low-dimensional task construction strategy is designed to transform the high-dimensional ROCCH maximization task into several low-dimensional tasks. Then, each low-dimensional task evolves with a population. To ensure that the low-dimensional task achieves a better ROCCH, an FM-based evolutionary multitasking operator is proposed. Specifically, for each low-dimensional task i, the element FM(i,j) in feedback matrix is defined to measure the degree that the low-dimensional task j could assist task i. Based on it, an FM-based assisted task selection operator and an FM-based knowledge transfer operator are developed to constitute the evolutionary multitasking operator, with which the useful knowledge is transferred among the low-dimensional tasks. After the evolution, the best ROCCHs obtained by the low-dimensional tasks are combined together to achieve the final ROCCH on the original high-dimensional task. Experiments on twelve high-dimensional datasets with different characteristics demonstrate the superiority of the proposed FM-EMTA over the state-of-the-arts in terms of the area under ROCCH, the hypervolume indicator and the running time.
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基于反馈矩阵的高维 ROC 凸壳最大化进化多任务算法
多目标进化算法在求解 ROC 凸壳最大化方面显示了其竞争力。然而,由于 "维度诅咒",很少有进化算法关注高维 ROCCH 最大化。因此,本文提出了一种基于反馈矩阵(FM)的进化多任务算法,称为 FM-EMTA。在 FM-EMTA 算法中,为解决 "维度诅咒 "问题,设计了一种基于特征重要性的低维任务构建策略,将高维 ROCCH 最大化任务转化为多个低维任务。然后,每个低维任务与一个群体一起演化。为确保低维任务实现更好的 ROCCH,提出了一种基于调频的进化多任务算子。具体来说,对于每个低维任务 i,定义反馈矩阵中的元素 FM(i,j),以衡量低维任务 j 对任务 i 的辅助程度。在此基础上,开发了基于调频的辅助任务选择算子和基于调频的知识转移算子,构成了进化多任务算子,有用的知识通过该算子在低维任务间转移。进化完成后,低维任务获得的最佳 ROCCH 将被组合在一起,以实现原始高维任务的最终 ROCCH。在 12 个具有不同特征的高维数据集上进行的实验证明,所提出的 FM-EMTA 在 ROCCH 下面积、超体积指标和运行时间方面都优于同行。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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