离散型增强烟花算法和kNN在特征选择中的研究  

Research on Discrete Enhanced Fireworks Algorithm and kNN in Feature Selection

作　　者：黄欣[1] 莫海淼 赵志刚[3] 曾敏 HUANG Xin;MO Haimiao;ZHAO Zhigang;ZENG Min(Department of Information and Electromechanical Engineering,Guangxi Agriculture Vocational and Technical College,Nanning 530007,China;Research Institute of Computer Network System,School of Management,Hefei University of Technology,Hefei 230009,China;College of Computer and Electronics Information,Guangxi University,Nanning 530004,China)

机构地区：[1]广西农业职业技术学院信息与机电工程系,南宁530007 [2]合肥工业大学管理学院计算机网络系统研究所,合肥230009 [3]广西大学计算机与电子信息学院,南宁530004

出　　处：《计算机工程与应用》2020年第16期112-117,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61363067);广西2016年度中青年教师基础能力提升项目(No.KY2016YB684)。

摘　　要：特征选择是从原始特征集中选取特征子集,并且降低特征维度和减少冗余信息,从而达到提高分类准确度的效果。为了达到此效果,提出了新的特征选择算法。该算法使用经过离散化处理之后的增强烟花算法来搜索特征子集,同时将特征子集和经过惩罚因子处理之后约束条件融入到目标函数中,然后将搜索到的特征子集的数据放到kNN分类器进行训练和预测,最后使用十折交叉验证来检验分类的准确性。使用UCI数据进行仿真实验,仿真结果表明:与引导型烟花算法、烟花算法、蝙蝠算法、乌鸦算法、自适应粒子群算法相比,所提算法的总体性能优于其他五种算法。Feature selection is to select feature subsets from the original feature set,and it can reduce the dimension of feature and redundant information,so as to improve the accuracy of classification.In order to achieve this effect,a new feature selection algorithm is proposed in this paper.The algorithm uses the enhanced fireworks algorithm after discretization to search the feature subset.At the same time,the feature subset and the constraint conditions after penalty factor processing are integrated into the objective function.Then the data of the feature subset are trained and predicted by the kNN classifier.Finally,the accuracy of classification is tested by 10-fold cross validation.Compared with the guided fireworks algorithm,fireworks algorithm,bat algorithm,crow search algorithm and adaptive particle swarm optimization algorithm,the simulation results using UCI data show that the overall performance of the proposed algorithm is better than that of the other five algorithms.

关 键 词：离散型增强烟花算法 特征选择 降维 分类 k近邻(kNN) 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]