Prediction accuracy measurements for ensemble classifier - Abdullah, ,; Ku-Mahamud, Ku Ruhana (2012)
Multiple classifier combination (or ensemble method) has been shown to be very helpful in improving the performance of classification over single classifier approach. The diversity among base classifiers (or ensemble members) is important when constructing a classifier ensemble.Although there have been several measures of diversity, but there is no reliable measure that can predict the ensemble accuracy. The base classifiers accuracy will increase when the diversity decreases and this is known as the accuracy-diversity dilemma.This paper presents a new method to measure diversity in classifier ensembles.Furthermore another parameter which based on this diversity measure is defined.It is hope that the new parameter will be able to predict the ensemble accuracy.Based on experimental results on classification of 84 samples of fruit images using nearest mean classifier ensembles, it has been shown that there is a positive linear relationship between the new parameter and the ensemble accuracy.This parameter is expected to assist in constructing diverse and accurate ensemble.