A reliable composite classification strategy

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 analysing the weighted combination of individual classifier decisions. The proposed algorithm is evaluated on both simulated data and on a biological cell classification problem and it is shown that improved accuracy is obtained when compared to that of the most accurate classifier.