An inductive learning method for medical diagnosis
Pattern Recognition Letters
A Noise Filtering Method for Inductive Concept Learning
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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In this paper, we present a class of test feature classifiers (TFCs). We discuss the properties and performance of the proposed classifiers and describe cases when a 100% recognition rate on test data can be achieved. When the number of features increases, classes having no more than a polynomial number of instances (in the number of features) are the only cases possible to process. We prove that for almost all pairs of classes with a polynomial number of instances, a 100% recognition rate on any test data can be achieved. To test the performance of the classifiers, we apply them to both artificial and real data. For the real data, we use the well known breast cancer, phoneme, and satimage databases, which are recognized to be difficult classification problems. Our experimental results show that the proposed classifiers not only have a high recognition ability but, also, the ability to achieve a 100% recognition rate in difficult classification problems