CAJ | 학술논문

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。
다류분류시목표식별중필수면대적일개관건문제，현유분류기대도위이분기，무법만족대다류목표진행분류，위차，제출이용규착수출편마방법대다류문제진행분해，즉파다류문제전화성이류문제；동시토론일충기우최소이승법대이분기결과진행융합적책략。실험분별대UCI수거집화삼충일유거리상수거집진행측시，결과표명여경전적다분류기상비，제출적다류분류책략유교고적분류정학솔。
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 classifi-cations. 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.