Reliability control in committee classifier environment
Vladimir Radevski, Younès Bennani · 2000
A classifier's ability to respond to novel patterns is not unique, and different classifiers provide different generalization. We investigate the co-operation of two neural network (NN) MLP-based classifiers (with two different feature sets as entries), through a committee classifier implementing a modified generalized committee principle for the combined decision. The training and test phase are performed on the data extracted from the NIST database. A rejection criteria is implemented and the final decision of the committee classifier integrates the additional information derived from the output of the trained NN member classifiers. The final classification system is a multistage system integrating the rule-based reasoning with improved recognition and reliability rates.