To minimize “0-1” loss, most of conventional classification algorithms non-explicitly assume that all results of
classification are accepted. However, the assumption is inapplicability to knowledge extraction in such fields as medical/fault
diagnosis and fraud/intrusion detection. Therefore, the binary classification problem with class-dependent reject cost(BCPCRC)
is summarized and is simplified, on basis of which the algorithm based on cost-sensitive support vector machines
with CRC(CSVM-CRC) is formulated. The CSVM-CRC algorithm involves training a classifier based on SVM algorithm,
computing the post probability of each sample, estimating the classification reliability of each sample, and determining the
optimal reject threshold. The experiment results show that the CSVM-CRC algorithm can reduce the average cost effectively.