  In ECOC framework, the ternary coding strategy is widely deployed in coding
process. It relabels classes with {"-1,0,1" }, where -1/1 means to assign the
corresponding classes to the negative/positive group, and label 0 leads to
ignore the corresponding classes in the training process. However, the
application of hard labels may lose some information about the tendency of
class distributions. Instead, we propose a Centroid distance-based Soft coding
scheme to indicate such tendency, named as CSECOC. In our algorithm, Sequential
Forward Floating Selection (SFFS) is applied to search an optimal class
assignment by minimizing the ratio of intra-group and inter-group distance. In
this way, a hard coding matrix is generated initially. Then we propose a
measure, named as coverage, to describe the probability of a sample in a class
falling to a correct group. The coverage of a class a group replace the
corresponding hard element, so as to form a soft coding matrix. Compared with
the hard ones, such soft elements can reflect the tendency of a class belonging
to positive or negative group. Instead of classifiers, regressors are used as
base learners in this algorithm. To the best of our knowledge, it is the first
time that soft coding scheme has been proposed. The results on five UCI
datasets show that compared with some state-of-art ECOC algorithms, our
algorithm can produce comparable or better classification accuracy with small
scale ensembles.
