Department of Computer Science & Engineering, Centurion University of Technology & Management, Bhubaneswar 761211, Odisha, India.
Department of Computer Application, VSSUT, Burla 768018, Odisha, India.
Sensors (Basel). 2022 Nov 30;22(23):9321. doi: 10.3390/s22239321.
In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.
近年来，由于高维数据集和不平衡数据集的存在，检测信用卡欺诈交易一直是一项具有挑战性的任务。从高维数据集中选择一组重要的特征已被证明是解决高维数据集问题的最突出方法，而特征的选择对于提高分类性能（如欺诈交易识别过程）至关重要。为了为该领域做出贡献，本文提出了一种基于元启发式算法（称为岩蹄兔群优化特征选择（RHSOFS）的新特征选择（FS）方法，该方法受到了岩蹄兔群在自然界中的行为的启发，并实施了监督机器学习技术来改进信用卡欺诈交易识别方法。该方法用于从高维数据集中选择一组最优相关特征的子集。在比较效率分析中，将 RHSOFS 与差分进化特征选择（DEFS）、遗传算法特征选择（GAFS）、粒子群优化特征选择（PSOFS）和蚁群优化特征选择（ACOFS）进行了比较效率分析。根据实验结果，RHSOFS 优于现有的方法，如 DEFS、GAFS、PSOFS 和 ACOFS。已经使用了各种统计检验来验证所提出模型的统计显著性。
