Evolving ensemble of classifiers in low-dimensional spaces using multi-objective evolutionary approach

Abstract

In this paper we discuss a new strategy to create ensemble of classifiers based on 
the multi objective evolutionary optimization. Instead of using feature selection 
technique which has been widely used in multi objective evolutionary approaches 
for ensemble generating, we have used a bagging-and-boosting-like strategy which 
also covers problems with lower dimensional feature spaces in which using feature 
selection technique may lead to ambiguous subspaces. After creating classifiers based 
on the amount of error created for each class, a multi-objective genetic algorithm has 
used to combine them to provide a set of powerful ensembles. Comprehensive experiments 
demonstrate the effectiveness of the proposed strategy.