基于多目标骨架粒子群优化的特征选择算法

针对在分类问题中，数据之间存在大量的冗余特征，不仅影响分类的准确性，而且会降低分类算法执行速度的问题，提出了一种基于多目标骨架粒子群优化（BPSO）的特征选择算法，以获取在特征子集个数与分类精确度之间折中的最优策略。为了提高多目标骨架粒子群优化算法的效率，首先使用了一个外部存档，用来引导粒子的更新方向；然后通过变异算子，改善粒子的搜索空间；最后，将多目标骨架粒子群算法应用到特征选择问题中，并利用K近邻（KNN）分类器的分类性能和特征子集的个数作为特征子集的评价标准，对UCI数据集以及基因表达数据集的12个数据集进行实验。实验结果表明，所提算法选择的特征子集具有较好的分类性能，最小分类错误率最大可以降低7.4%，并且分类算法的执行时间最多能缩短12 s，能够有效提高算法的分类性能与执行速度。

Feature selection algorithm based on multi-objective bare-bones particle swarm optimization

Concerning there are a lot of redundant features classified in data which not only affect the classification accuracy, but also reduce classification speed, a feature selection algorithm based on multi-objective Bare-bones Particle Swarm Optimization (BPSO) was proposed to obtain the tradeoff between the number of feature subsets and the classification accuracy. In order to improve the efficiency of the multi-objective BPSO, firstly an external archive was used to guide the update direction of the particle, and then the search space of the particle was improved by a mutation operator. Finally, the multi-objective BPSO was applied to feature selection problems, and the classification performance and the number of selected features of the K Nearest Neighbors (KNN) classifier were used as feature selection criteria. The experiments were performed on 12 datasets of UCI datasets and gene expression datasets. The experimental results show that the feature subset selected by the proposed algorithm has better classification performance, the maximum error rate of the minimum classification can be reduced by 7.4%, and the maximum execution speed of the classification algorithm can be shortened by 12 s at most.