A composite classification scheme is proposed by combining several classifiers with distinctly different design methodologies. The classifiers are selected from a number of state of the art pattern classification schemes with a view to obtain superior performance. In this scheme, no a priori information except a set of pre-classified data is assumed to be available. By using distinctly different classifiers, the common mode data misclassification is reduced. Traditionally, after the design and evaluation phase, the pre-classified data is discarded. In this scheme, however, the misclassified data from each classifier in the training set is tagged and stored with a view to weight the decisions of the classifiers. If a given test sample is close to a misclassified data cluster of a particular classifier, then the decision made by this classifier is given a lower weighting. The final decision is made by analyzing the weighted combination of individual classifier decisions. The proposed algorithm is evaluated on both simulated data and a biological cell classification problem and it is shown that improved accuracy is obtained when compared to that of the most accurate classifier.

Proceedings of the 1998 11th Canadian Conference on Electrical and Computer Engineering, CCECE. Part 2 (of 2)
Department of Systems and Computer Engineering

Balasubramanian, R. (R.), Rajan, S, Doraiswami, R. (R.), & Stevenson, M. (M.). (1998). Reliable composite classification strategy. In Canadian Conference on Electrical and Computer Engineering (pp. 914–917).