Dynamic base classifier pool for classifier selection in Multiple Classifier Systems

Abstract

Multiple Classifier Systems (MCSs) are a method combining decisions of base classifiers. The set of the base classifiers is fixed in traditional MCSs. When applying MCSs in online learning environment, the base classifiers have to be updated frequently to adapt the change of the environment. However, updating classifiers is time consumed, especially when the number of base classifier is big. Therefore, a selection method with dynamic base classifier pool is proposed in this paper. Rather than updating the existing base classifiers, a new base classifier is added to MCSs. The new base classifier is trained by using the samples which far away from the training set. Experimental results show that that the proposed method outperforms the MCSs with the fix base classifier pool in term of accuracy.