  Class imbalance is one of the challenging problems for machine learning in
many real-world applications, such as coal and gas burst accident monitoring:
the burst premonition data is extreme smaller than the normal data, however,
which is the highlight we truly focus on. Cost-sensitive adjustment approach is
a typical algorithm-level method resisting the data set imbalance. For SVMs
classifier, which is modified to incorporate varying penalty parameter(C) for
each of considered groups of examples. However, the C value is determined
empirically, or is calculated according to the evaluation metric, which need to
be computed iteratively and time consuming. This paper presents a novel
cost-sensitive SVM method whose penalty parameter C optimized on the basis of
cluster probability density function(PDF) and the cluster PDF is estimated only
according to similarity matrix and some predefined hyper-parameters.
Experimental results on various standard benchmark data sets and real-world
data with different ratios of imbalance show that the proposed method is
effective in comparison with commonly used cost-sensitive techniques.
