An Ensemble Pruning Method Based on Class and Inter-Class Competences for Computer-Aided Medical Decisions

Classifier ensemble pruning is a technique to increase ensemble accuracy and reduce its size. In the paper we propose a novel method of ensemble pruning which is based on the local class and inter-class competences of base classifiers for the test object. In the first step, using the inter-class competences, we identify classifiers that tend to commit the same type of errors. The identified classifiers create a common cluster. In the second step, classifiers in each cluster are ordered according to the class competence and then the top-k ones are selected from each cluster as the final ensemble. Experimental investigations of the proposed pruning method for two homogeneous pools of base classifiers and for three values of parameter k were conducted. In experimental studies we used 16 real data sets related to the various medical decision-making problems. Obtained results clearly show that the use of the ensemble pruning procedure leads to the better classification accuracy.