Random Forest for Reliable Pre-Classification of Handwritten Characters

In many real world applications, the cost of a wrong decision may be much higher than the benefit of having a high classification rate. This kind of classification problems represent very challenging tasks, because they require highly reliable systems and may benefit of introducing a reject option. To this purpose, many classification systems have been proposed, among which classifier ensembles represent a successful example. This approach aims at combining classifiers making uncorrelated errors. In this framework, the Random Forest (RF) represents a case of special interest. A RF is made of a suitable ensemble of decision trees and has proved to be very effective in several fields. In this paper, the RF classification reliability is experimentally analyzed with reference to the case of handwriting recognition. The aim is to verify if such reliability can be effectively used to introduce a new reject option, which considers, for each unknown sample, a subset of few classes including with high probability the correct one. The whole system operates as a pre-classification stage and the reject option should allow us to obtain a low error rate without significantly affecting the recognition rate. Experiments, carried out on two real world datasets, have shown the effectiveness of the proposed method.