  A group of transition probability functions form a Shannon's channel whereas
a group of truth functions form a semantic channel. Label learning is to let
semantic channels match Shannon's channels and label selection is to let
Shannon's channels match semantic channels. The Channel Matching (CM) algorithm
is provided for multi-label classification. This algorithm adheres to maximum
semantic information criterion which is compatible with maximum likelihood
criterion and regularized least squares criterion. If samples are very large,
we can directly convert Shannon's channels into semantic channels by the third
kind of Bayes' theorem; otherwise, we can train truth functions with parameters
by sampling distributions. A label may be a Boolean function of some atomic
labels. For simplifying learning, we may only obtain the truth functions of
some atomic label. For a given label, instances are divided into three kinds
(positive, negative, and unclear) instead of two kinds as in popular studies so
that the problem with binary relevance is avoided. For each instance, the
classifier selects a compound label with most semantic information or richest
connotation. As a predictive model, the semantic channel does not change with
the prior probability distribution (source) of instances. It still works when
the source is changed. The classifier changes with the source, and hence can
overcome class-imbalance problem. It is shown that the old population's
increasing will change the classifier for label "Old" and has been impelling
the semantic evolution of "Old". The CM iteration algorithm for unseen instance
classification is introduced.
