Learning and classification of monotonic ordinal concepts
Computational Intelligence
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Decision trees for ordinal classification
Intelligent Data Analysis
On the random generation of monotone data sets
Information Processing Letters
Adding monotonicity to learning algorithms may impair their accuracy
Expert Systems with Applications: An International Journal
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
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Monotone constraints are very common while dealing with multi-attribute ordinal problems. Grinding wheels hardness selection, timely replacements of costly laser sensors in silicon wafer manufacturing, and the selection of the right personnel for sensitive production facilities, are just a few examples of ordinal problems where monotonicity makes sense. In order to evaluate the performance of various ordinal classifiers one needs both artificially generated as well as real world data sets. Two algorithms are presented for generating monotone ordinal data sets. The first can be used for generating random monotone ordinal data sets without an underlying structure. The second algorithm, which is the main contribution of this paper, describes for the first time how structured monotone data sets can be generated.