基于子类划分和粒子群优化的自适应编码多类分类方法  

Multiclass classification of adaptive error-correcting output codes based on subclass and particle swarm optimization

作　　者：薛爱军[1] 王晓丹[1] 

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《控制与决策》2018年第2期211-218,共8页Control and Decision

基　　金：国家自然科学基金项目(61273275)

摘　　要：纠错输出编码(ECOC)可以有效地解决多类分类问题.基于数据的编码是主要的编码方法之一.对此,提出一种基于子类划分和粒子群优化(PSO)的自适应编码方法,利用混淆矩阵衡量各类别的相关性,基于规则的方法对类别进行自适应组合,根据组合方案构建类别的二类划分并最终形成编码矩阵,通过引入PSO算法寻找最优阈值,从而得到最优编码矩阵.实验结果表明,所提出的编码方法可以得到更好的分类性能.Error correcting output codes（ECOC） is an effective way to solve multiclass classification problems. Encoding based on data is one of important methods to having coding matrix. Thereforem, an adaptively encoding method based on subclass and particle swarm optimization（PSO） is proposed. Firstly, the similarity between each pair of patterns is measured by using the confusion matrix, and classes are regrouped based on rules. Then binary partitions are gotten based on pattern combination. Finally, the PSO algorithm is introduced to find the most suitable thresholds, thus obtaining a data driven coding matrix. Experimental results show that the proposed method can provide better performance.

关 键 词：模式识别 纠错输出编码 多类分类 子类划分 粒子群优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]