Design of multiple classifier systems based on testing sample pairs

A new multiple classifier system (MCS) is proposed based on CTSP (classification based on Testing Sample Pairs), which is a kind of applicable and efficient classification method. However, the original output form of the CTSP is only crisp class labels. To make use of the information provided by the classifier, in this paper, the output of CTSP is modeled using the membership function. Then, the fuzzy-cautious ordered weighted averaging approach with evidential reasoning (FCOWA-ER) is used to combine the membership functions originated from different member classifiers. It is shown by experimental results that the proposed MCS effectively can improve the classification performance.