Multi-classification based on least square and error-correcting output codes

Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (7): 190-193.

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Multi-classification based on least square and error-correcting output codes

WANG Qiang1, LIU Xiaodong1, GAO Jie2, MI Yu3

1.Engineering College, Air Force University of Engineering, Xi’an 710038, China
2.631 Military Affairs Delegate Studio, Xi’an 710077, China
3.Martial Deputy of PLA on Factory 786, China

Online:2014-04-01 Published:2014-04-25

基于最小二乘和纠错输出编码的多类分类

王强1，刘晓东1，高洁2，米裕3

1.空军工程大学工程学院，西安 710038
2.中航工业631所，西安 710077
3.中国人民解放军驻786厂军事代表室

Abstract

Abstract: Multi-classification is the key issue in target recognition. The dichotomies so far is mostly designed for binary classification, which cannot meet the requirement of the multi-class target recognition. To solve this problem, the ECOC（Error Correcting Output Codes） is used to decompose a complex multi-classification problem into a set of binary classifications. At the same time, a decoding strategy based on least square method is proposed to fusion the dichotomies’ results. The experiments based on UCI and three kinds of different HRRPs validate that compared to the state-of-the-art dichotomies, the approach presented has better classification performance.

Key words: pattern recognition, multi-classification, Error-Correcting Output Codes（ECOC）, least square

摘要： 多类分类是目标识别中必须面对的一个关键问题，现有分类器大都为二分器，无法满足对多类目标进行分类，为此，提出利用纠错输出编码方法对多类问题进行分解，即把多类问题转化成二类问题；同时讨论一种基于最小二乘法对二分器结果进行融合的策略。实验分别对UCI数据集和三种一维距离像数据集进行测试，结果表明与经典的多分类器相比，提出的多类分类策略有较高的分类正确率。

关键词: 模式识别, 多类分类, 纠错输出编码, 最小二乘

WANG Qiang1, LIU Xiaodong1, GAO Jie2, MI Yu3. Multi-classification based on least square and error-correcting output codes[J]. Computer Engineering and Applications, 2014, 50(7): 190-193.

王强1，刘晓东1，高洁2，米裕3. 基于最小二乘和纠错输出编码的多类分类[J]. 计算机工程与应用, 2014, 50(7): 190-193.

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Multi-classification based on least square and error-correcting output codes

基于最小二乘和纠错输出编码的多类分类

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