CAJ | 학술논문

针对多类分类问题,利用纠错输出编码作为分解框架,把多类问题转化为多个二类问题加以解决;同时提出一种基于证据理论的解码策略,把每一个二分器的输出作为证据之一进行融合,并讨论在两种编码类型(二元和三元编码矩阵)下证据融合的不同策略.通过实验分别对UCI数据集和3种一维距离像数据集进行测试,并与几种经典的解码方法进行比较,验证了所提出的方法能有效提高纠错输出编码特别是三元编码矩阵的分类正确率.
침대다류분류문제,이용규착수출편마작위분해광가,파다류문제전화위다개이류문제가이해결;동시제출일충기우증거이론적해마책략,파매일개이분기적수출작위증거지일진행융합,병토론재량충편마류형(이원화삼원편마구진)하증거융합적불동책략.통과실험분별대UCI수거집화3충일유거리상수거집진행측시,병여궤충경전적해마방법진행비교,험증료소제출적방법능유효제고규착수출편마특별시삼원편마구진적분류정학솔.
@@@@To model multi-class classification problems, error correcting output codes(ECOC) are used as decomposing frame to reduce multi-class to binary. A decoding strategy based on DS evidence theory is proposed, which takes every binary learner’s output as evidence to fusion and discusses different DS evidence fusion strategies based on two kinds of coding type(binary ECOC and ternary ECOC). Experimental results on UCI and three kinds of HRRP show that the proposed scheme provides better performance of error correcting output codes especially ternary ECOC than other state-of-the-art encoding strategies.