Classifier Combination : the Role of a - Priori Knowledge

The aim of this paper is to investigate the role of the a-priori knowledge in the process of classifier combination. For this purpose three combination methods are compared which use different levels of a-priori knowledge. The performance of the methods are measured under different working conditions by simulating sets of classifier with different characteristics. For this purpose, a random variable is used to simulate each classifier and an estimator of stochastic correlation is used to measure the agreement among classifiers. The experimental results, which clarify the conditions under which each combination method provides better performance, show to what extend the a-priori knowledge on the characteristics of the set of classifiers can improve the effectiveness of the process of classifier combination.