Algorithm selection for classification problems

A number of algorithms are available in the areas of data mining, machine learning and pattern recognition for solving the same kind of problem. But there is a little guidance for suggesting algorithm to use which gives best results for the problem at hand. This paper shows an approach for solving this problem using meta-learning. The paper uses three types of data characteristics. Simple, information theoretic, and statistical data characteristics are used. Results are generated using nine different algorithms on thirty eight benchmark datasets from UCI repository. The proposed approach uses K-nearest neighbor algorithm for suggesting the suitable algorithm. Classifier accuracy is taken as a basis for recommending the algorithm. By using meta-learning, accurate method can be recommended as per the given data, and cognitive overload for applying each method, comparing with other methods and then selecting the suitable method for use can be reduced. Thus it helps in adaptive learning methods. The experimentation shows that predicted accuracies are matching with the actual accuracies for more than 90 % of the benchmark datasets used. Thus it is concluded that the number of attributes, the number of instances, the number of classes, maximum probability of class and class entropy are playing a major role in classifier accuracy and algorithm selection for thirty eight datasets used for experimentation.