Learning and classification of monotonic ordinal concepts
Computational Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
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Information and Computation
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Annals of Mathematics and Artificial Intelligence
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Machine Learning
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Fundamenta Informaticae - Fundamentals of Knowledge Technology
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RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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IEEE Transactions on Neural Networks
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Fundamenta Informaticae - Fundamentals of Knowledge Technology
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Neural Computation
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For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. A distinction is made between methods that work only for monotone datasets and methods that work for monotone and non-monotone datasets alike.