Learning and classification of monotonic ordinal concepts
Computational Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Rough Sets: Theoretical Aspects of Reasoning about Data
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Fundamenta Informaticae - Intelligent Systems
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Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
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In many classification problems the domains of the attributes and the classes are linearly ordered. For such problems the classification rule often needs to be order-preserving or monotonic as we call it. Since the known decision tree methods generate non-monotonic trees, these methods are not suitable for monotonic classification problems. We provide an order-preserving tree-generation algorithm for multi-attribute classification problems with $k$ linearly ordered classes, and an algorithm for repairing non-monotonic decision trees. The performance of these algorithms is tested on a real-world financial dataset and on random monotonic datasets.