A COMPARISON STUDY OF SOME COMBINED CLASSIFIERS
Majid Mojirsheibani · Communications in Statistics - Simulation and Computation · 2002
In this article we consider two methods for combining a number of individual classifiers in order to construct more effective classification rules. The effectiveness of these methods, as measured by a comparison of their misclassification error rates with those of the individual classifiers, is assessed via a number of examples that involve simulated data. We also compare the results to those of two existing combining procedures.