On the correlation between genotype and classifier diversity

Diversity is a key element in the success of classifier ensembles, and has attracted much recent attention. It is typically measured by directly computing the amount of disagreement between ensemble classifiers at the decision level. This costly process usually involves evaluating output predictions of each classifier over some validation data set. Since most statistical and neural network classifiers can adjust internal learning dynamics by varying their hyperparameter values (corresponding to genotype values), this information can also provide an estimate of diversity. This paper measures the correlation between genotype and classifier diversity among an ensemble of fuzzy ARTMAP neural network classifiers applied to video face recognition. It is empirically shown that as genotype diversity increases within an ensemble, classifier diversity also significantly increases. This correlation can then be exploited to measure the diversity among base classifiers during ensemble design with a significantly lower computational cost.