CLASSIFIER COMBINATION: THE ROLE OF A­PRIORI KNOWLEDGE

V.DI LECCE1, G.DIMAURO2, A.GUERRIERO1, S.IMPEDOVO2, G.PIRLO2, A.SALZO2

1Dipartimento di Ing. Elettronica ­Politecnico di Bari­ via Re David ­70126 Bari­ Italy
2Dipartimento di Informatica ­ Universitą di Bari ­ Via Orabona, 4 ­ 70126 Bari -- Italy

The aim of this paper is to investigate the role of the a­priori knowledge in the process of classifier combination. For this purpose three combination methods are compared which use different levels of a­priori knowledge. The performance of the methods is 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 a­priori knowledge on the characteristics of the set of classifiers can improve the effectiveness of the process of classifier combination.

In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 143-152.