College of Science, Liaoning Technical University, Fuxin, Liaoning, China.
College of Mines, Liaoning Technical University, Fuxin, Liaoning, China.
PLoS One. 2022 May 19;17(5):e0267041. doi: 10.1371/journal.pone.0267041. eCollection 2022.
Feature selection (FS) can eliminate many redundant, irrelevant, and noisy features in high-dimensional data to improve machine learning or data mining models' prediction, classification, and computational performance. We proposed an improved whale optimization algorithm (IWOA) and improved k-nearest neighbors (IKNN) classifier approaches for feature selection (IWOAIKFS). Firstly, WOA is improved by using chaotic elite reverse individual, probability selection of skew distribution, nonlinear adjustment of control parameters and position correction strategy to enhance the search performance of the algorithm for feature subsets. Secondly, the sample similarity measurement criterion and weighted voting criterion based on the simulated annealing algorithm to solve the weight matrix M are proposed to improve the KNN classifier and improve the evaluation performance of the algorithm on feature subsets. The experimental results show: IWOA not only has better optimization performance when solving benchmark functions of different dimensions, but also when used with IKNN for feature selection, IWOAIKFS has better classification and robustness.
特征选择（FS）可以消除高维数据中的许多冗余、不相关和嘈杂的特征，从而提高机器学习或数据挖掘模型的预测、分类和计算性能。我们提出了一种改进的鲸鱼优化算法（IWOA）和改进的 K-最近邻（IKNN）分类器方法用于特征选择（IWOAIKFS）。首先，通过使用混沌精英反向个体、斜度分布的概率选择、控制参数的非线性调整和位置校正策略来改进 WOA，以增强算法对特征子集的搜索性能。其次，提出了基于模拟退火算法的样本相似性度量标准和加权投票标准来解决权重矩阵 M，以改进 KNN 分类器，并提高算法对特征子集的评估性能。实验结果表明：IWOA 不仅在解决不同维度的基准函数时具有更好的优化性能，而且在与 IKNN 一起用于特征选择时，IWOAIKFS 具有更好的分类和鲁棒性。
