Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia.
Department of Applied Cybernetics,Faculty of Science, University of Hradec Kràlové, Hradec Kràalové, Czech Republic.
PLoS One. 2022 Oct 10;17(10):e0275727. doi: 10.1371/journal.pone.0275727. eCollection 2022.
The fast-growing quantity of information hinders the process of machine learning, making it computationally costly and with substandard results. Feature selection is a pre-processing method for obtaining the optimal subset of features in a data set. Optimization algorithms struggle to decrease the dimensionality while retaining accuracy in high-dimensional data set. This article proposes a novel chaotic opposition fruit fly optimization algorithm, an improved variation of the original fruit fly algorithm, advanced and adapted for binary optimization problems. The proposed algorithm is tested on ten unconstrained benchmark functions and evaluated on twenty-one standard datasets taken from the Univesity of California, Irvine repository and Arizona State University. Further, the presented algorithm is assessed on a coronavirus disease dataset, as well. The proposed method is then compared with several well-known feature selection algorithms on the same datasets. The results prove that the presented algorithm predominantly outperform other algorithms in selecting the most relevant features by decreasing the number of utilized features and improving classification accuracy.
快速增长的信息量阻碍了机器学习的进程，使其计算成本高，结果不达标。特征选择是一种预处理方法，用于从数据集中获取最优的特征子集。优化算法在降低高维数据集的维度的同时，难以保持准确性。本文提出了一种新颖的混沌反对果蝇优化算法，这是对原始果蝇算法的改进，适用于二进制优化问题。所提出的算法在十个无约束基准函数上进行了测试，并在加利福尼亚大学欧文分校和亚利桑那州立大学存储库中选取的二十一个标准数据集上进行了评估。此外，还对冠状病毒疾病数据集进行了评估。然后，该方法与同一数据集上的几种著名的特征选择算法进行了比较。结果证明，所提出的算法通过减少使用的特征数量和提高分类准确性，在选择最相关的特征方面优于其他算法。
