基于邻域粗糙集和帝王蝶优化的特征选择算法

针对经典的帝王蝶优化（MBO）算法不能很好地处理连续型数据，以及粗糙集模型对于大规模、高维复杂的数据处理能力不足等问题，提出了基于邻域粗糙集（NRS）和MBO的特征选择算法。首先，将局部扰动和群体划分策略与MBO算法结合，并构建传输机制以形成一种二进制MBO（BMBO）算法；其次，引入突变算子增强算法的探索能力，设计了基于突变算子的BMBO（BMBOM）算法；然后，基于NRS的邻域度构造适应度函数，并对初始化的特征子集的适应度值进行评估并排序；最后，使用BMBOM算法通过不断迭代搜索出最优特征子集，并设计了一种元启发式特征选择算法。在基准函数上评估BMBOM算法的优化性能，并在UCI数据集上评价所提出的特征选择算法的分类能力。实验结果表明，在5个基准函数上，BMBOM算法的最优值、最差值、平均值以及标准差明显优于MBO和粒子群优化（PSO）算法；在UCI数据集上，与基于粗糙集的优化特征选择算法、结合粗糙集与优化算法的特征选择算法、结合NRS与优化算法的特征选择算法、基于二进制灰狼优化的特征选择算法相比，所提特征选择算法在分类精度、所选特征数和适应度值这3个指标上表现良好，能够选择特征数少且分类精度高的最优特征子集。

Feature selection algorithm based on neighborhood rough set and monarch butterfly optimization

The classical Monarch Butterfly Optimization （MBO） algorithm cannot handle continuous data well， and the rough set model cannot sufficiently process large-scale， high-dimensional and complex data. To address these problems， a new feature selection algorithm based on Neighborhood Rough Set （NRS） and MBO was proposed. Firstly， local disturbance， group division strategy and MBO algorithm were combined， and a transmission mechanism was constructed to form a Binary MBO （BMBO） algorithm. Secondly， the mutation operator was introduced to enhance the exploration ability of this algorithm， and a BMBO based on Mutation operator （BMBOM） algorithm was proposed. Then， a fitness function was developed based on the neighborhood dependence degree in NRS， and the fitness values of the initialized feature subsets were evaluated and sorted. Finally， the BMBOM algorithm was used to search the optimal feature subset through continuous iterations， and a meta-heuristic feature selection algorithm was designed. The optimization performance of the BMBOM algorithm was evaluated on benchmark functions， and the classification performance of the proposed feature selection algorithm was evaluated on UCI datasets. Experimental results show that， the proposed BMBOM algorithm is significantly better than MBO and Particle Swarm Optimization （PSO） algorithms in terms of the optimal value， worst value， average value and standard deviation on five benchmark functions. Compared with the optimized feature selection algorithms based on rough set， the feature selection algorithms combining rough set and optimization algorithms， the feature selection algorithms combining NRS and optimization algorithms， the feature selection algorithms based on binary grey wolf optimization， the proposed feature selection algorithm performs well in the three indicators of classification accuracy， the number of selected features and fitness value on UCI datasets， and can select the optimal feature subset with few features and high classification accuracy.