A modified class-specific weighted soft voting for bagging ensemble
Laxmi Narayana Eeti, Krishna Mohan Buddhiraju · 2016
In ensemble methods applied to base classifiers that generate class probabilities, such classification outputs from individual ensemble members are combined using soft voting method. Weights are computed using an optimization function that is based upon the test performance of all trained ensemble members. However, in special case such as bagging method where samples are chosen randomly, there is chance of repetition of same samples, lacking of class-specific global representative samples which could influence the behavior of members. Aforementioned a priori information on representative samples could be used to reinforce or dilute weightage values for a particular class. The present paper is an attempt to explore the possibility of improving class-specific weighted soft voting using proportionality information derived from repetition and intra-class variability (global representation) of training samples. Additional class-specific weights are assigned in the computation of final decision. Preliminary results show slight improvement in accuracies.