A self-adaptive multi-objective feature selection approach for classification problems

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yu Xue, Hao Zhu, Ferrante Neri
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引用次数: 8

Abstract

In classification tasks, feature selection (FS) can reduce the data dimensionality and may also improve classification accuracy, both of which are commonly treated as the two objectives in FS problems. Many meta-heuristic algorithms have been applied to solve the FS problems and they perform satisfactorily when the problem is relatively simple. However, once the dimensionality of the datasets grows, their performance drops dramatically. This paper proposes a self-adaptive multi-objective genetic algorithm (SaMOGA) for FS, which is designed to maintain a high performance even when the dimensionality of the datasets grows. The main concept of SaMOGA lies in the dynamic selection of five different crossover operators in different evolution process by applying a self-adaptive mechanism. Meanwhile, a search stagnation detection mechanism is also proposed to prevent premature convergence. In the experiments, we compare SaMOGA with five multi-objective FS algorithms on sixteen datasets. According to the experimental results, SaMOGA yields a set of well converged and well distributed solutions on most data sets, indicating that SaMOGA can guarantee classification performance while removing many features, and the advantage over its counterparts is more obvious when the dimensionality of datasets grows.
分类问题的自适应多目标特征选择方法
在分类任务中,feature selection (FS)可以降低数据维数,也可以提高分类精度,这两者通常被视为FS问题的两个目标。许多元启发式算法已经被应用于解决FS问题,当问题相对简单时,它们的表现令人满意。然而,一旦数据集的维数增加,它们的性能就会急剧下降。本文提出了一种自适应多目标遗传算法(SaMOGA),该算法能够在数据集维数增加的情况下保持较高的性能。SaMOGA的主要思想是通过应用自适应机制,在不同的进化过程中动态选择五种不同的交叉算子。同时,提出了一种搜索停滞检测机制,防止算法过早收敛。在实验中,我们将SaMOGA与5种多目标FS算法在16个数据集上进行了比较。实验结果表明,SaMOGA在大多数数据集上得到了一组收敛性和分布良好的解决方案,表明SaMOGA在去除大量特征的同时,能够保证分类性能,并且随着数据集维数的增加,其优势更加明显。
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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