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Designing an optimal Classifier Ensemble for online character recognition using Genetic Algorithms

We formulate the problem of creating an optimal classifier ensemble as an optimization problem and apply genetic algorithms to the problem. A pool of 25 individual classifiers is created by training SVM-based classifiers on various features and by varying SVM kernel parameters. A subset of the classifiers selected from the above classifier pool, generated using the proposed optimization technique, constitute the final optimized classifier ensemble. The ensembles designed by the proposed method are applied to the problem of stroke recognition for two Indic scripts: Devnagari and Tamil. Ensemble performance always exceeded the performance of best individual classifier and is comparable to some of the best reported online character recognition results for the above scripts.