Zhou Yu, Kang Junhao, Zhang Xiao
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China.
Entropy (Basel). 2020 Jun 1;22(6):613. doi: 10.3390/e22060613.
Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.
最近基于离散化的特征选择方法通过引入基于熵的特征切点，将离散化和特征选择集成到一个阶段来处理高维数据，显示出巨大优势。然而，当前方法通常独立考虑单个特征，忽略了有切点特征和无切点特征之间的相互作用，从而导致信息丢失。在本文中，我们提出了一种基于遗传算法（GA）和粒子群优化（PSO）的协同进化算法，该算法同时搜索有和没有基于熵切点的特征子集。对于有切点的特征，使用一种排序机制来控制遗传算法中变异和交叉的概率。此外，应用二进制编码的粒子群优化来更新所选无切点特征的索引。在10个真实数据集上的实验结果验证了我们的算法与几个最先进的竞争对手相比在分类准确率方面的有效性。
