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1 A new ECOC encoding algorithm is proposed, which separates classes into two groups by maximizing the ratio of the inter-group distance to intra-group distance. 2 A novel Soft-Coded (SC) ECOC algorithm is proposed by softening the codewords of the ECOC codematrix by considering the subordination degree of each class to both groups. 3 A new measure coverage is designed to evaluate the subordination degrees of different classes to the positive and the negative groups, and the values are set as the elements of the codematrix. 4 A self-adaptive strategy is designed to adjust codewords, making them better fit the associated learners. Error-Correcting Output Codes (ECOC) algorithms enable multiclass classification by reassigning multiple classes to the positive/negative group with the class reassignment schemes being recorded as binary/ternary hard-coded (HC) codematrices. Different classes tend to get diverse subordination degrees to the positive/negative group, providing clues to correct potential errors. However, the HC codematrices are unable to provide the information in the subordination degrees. In this paper, a Soft-Coded ECOC (SC-ECOC) scheme, namely, the Sequential Forward Floating Selection algorithm, is proposed by filling codematrices with real values instead of hard codes to improve classification performance. This algorithm divides multiple classes into two groups by maximizing the ratio of inter-group distance to intra-group distance. Then a new measure coverage is designed to evaluate the subordination degrees of different classes to both groups, which are set as the elements to form a codematrix. Furthermore, a self-adaptive strategy adjusts the value of each element to fit learners better. Experiments are carried out to verify the performance of our algorithm on various data sets, and results confirm that our algorithm can achieve more balanced results compared with the traditional HC ECOC algorithms. Besides, the values of soft codes correlate with the difficulty level of various classes to improve the multiclass classification ability. [ABSTRACT FROM AUTHOR]