A multiple criteria ensemble pruning method for binary classification based on D-S theory of evidence.

Ensemble pruning becomes an important stage in multiple classifier systems, and it has been widely applied to solve binary classification problems. Diversity and performance measures are two widely used evaluation methods to build the selection criterion for ensemble pruning. However, few works consider both of them simultaneously, and they usually use one algorithm to measure the diversity or performance, which may not be enough to capture all the relevant diversities and performance of the base classifiers. To solve this problem, we propose a multiple criteria ensemble pruning method by employing multiple diversity and performance measures to capture the base classifiers' diversity and evaluate their classification ability respectively. Moreover, a multi-criteria decision making method, based on fuzzy soft set and Dempster-Shafer theory of evidence, is used to build the final selection criterion, which can make a good trade-off between the diversity and performance measures. With sixteen binary data sets, the experimental studies show its effectivity and superiority for ensemble pruning over six state-of-the-art benchmark methods. [ABSTRACT FROM AUTHOR]