基于自适应鲸鱼优化算法和容错邻域粗糙集的特征选择算法

针对传统鲸鱼优化算法(WOA)不能有效处理连续型数据、邻域粗糙集对噪声数据的容错性较差等问题,文中提出基于自适应WOA和容错邻域粗糙集的特征选择算法.首先,为了避免WOA过早陷入局部最优,基于迭代周期构建分段式动态惯性权重,改进WOA的收缩包围和螺旋捕食行为,设计自适应WOA.然后,为了解决邻域粗糙集对噪声数据缺乏容错性的问题,引入邻域内相同决策特征所占的比例,定义容错邻域上下近似集、容错近似精度和近似粗糙度、容错依赖度及容错近似条件熵.最后,基于容错邻域粗糙集构造适应度函数,使用自适应WOA,不断迭代以获取最优子群.高维数据集上采用费雪评分算法进行初步降维,降低算法的时间复杂度.在8个低维UCI数据集和6个高维基因数据集上的实验表明,文中算法可有效选择特征个数较少且分类精度较高的特征子集.

Feature Selection Based on Adaptive Whale Optimization Algorithm and Fault-Tolerance Neighborhood Rough Sets

Traditional whale optimization algorithm(WOA)cannot handle continuous data effectively,and the tolerance of neighborhood rough sets(NRS)for noise data is poor.To address the issues,an algorithm of feature selection based on adaptive WOA and fault-tolerance NRS is presented.Firstly,a piecewise dynamic inertia weight based on iteration cycle is proposed to prevent the WOA from falling into local optimum prematurely.The shrinkage enveloping and spiral predation behaviors of WOA are improved,and an adaptive WOA is designed.Secondly,the ratio of the same decision features in the neighborhood is introduced to make up for the fault tolerance lack of NRS model for noise data,and the upper and lower approximations,approximation precision and approximation roughness,fault-tolerance dependence and approximation conditional entropy of fault-tolerance neighborhood are defined.Finally,a fitness function is constructed based on the fault-tolerance NRS,and then the adaptive WOA searches for the optimal feature subset through continuous iterations.The Fisher score is employed to reduce the dimensions of high-dimensional datasets preliminarily and the time complexity of the proposed algorithm effectively.The proposed algorithm is tested on 8 low-dimensional UCI datasets and 6 high-dimensional gene datasets.Experimental results demonstrate that the proposed algorithm selects fewer features effectively with high classification accuracy.