摘要: We aim to find the effect of diversity on combiner performance. Three diversity measures are used to calculate the diversity of combined classifiers. We aim to identify which measure is closely related to the combiner performance.... 展开 We aim to find the effect of diversity on combiner performance. Three diversity measures are used to calculate the diversity of combined classifiers. We aim to identify which measure is closely related to the combiner performance. Three combiner types are used; Bagging and a conventional three classifier system, in which three classifier types are used; backpropagation neural network, bayesian and k-nearest neighbor classifiers. Additionally we experiment with a feature based combiner system proposed by Alkoot [13,14]. Results obtained on real data indicate the diversity measure to be higher for systems with higher classification rate, if it outperforms other classifiers by a large margin. Otherwise, if the performances of the compared systems are close the diversity measure may not be higher for the best system. On many occasions the diversity measures were not good indicators of system performance. On some instances we found that the more diverse system did not yield a better performance. 收起