Methods for pattern selection, class-specific feature selection and classification for automated learning

This paper presents methods for training pattern (prototype) selection, class-specific feature selection and classification for automated learning. For training pattern selection, we propose a method of sampling that extracts a small number of representative training patterns (prototypes) from the dataset. The idea is to extract a set of prototype training patterns that represents each class region in a classification problem. In class-specific feature selection, we try to find a separate feature set for each class such that it is the best one to separate that class from the other classes. We then build a separate classifier for that class based on its own feature set. The paper also presents a new hypersphere classification algorithm. Hypersphere nets are similar to radial basis function (RBF) nets and belong to the group of kernel function nets. Polynomial time complexity of the methods is proven. Polynomial time complexity of learning algorithms is important to the field of neural networks. Computational results are provided for a number of well-known datasets. None of the parameters of the algorithm were fine tuned for any of the problems solved and this supports the idea of automation of learning methods. Automation of learning is crucial to wider deployment of learning technologies.