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.