C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Decision trees for ordinal classification
Intelligent Data Analysis
Adding monotonicity to learning algorithms may impair their accuracy
Expert Systems with Applications: An International Journal
Spectrum of variable-random trees
Journal of Artificial Intelligence Research
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Rule and tree based classifier learning systems can employ the idea of order on discrete attribute and class values to aid in classification. Much work has been done on using both orders on class values and monotonic relationships between class and attribute orders. In contrast to this, we examine the usefulness of order specifically on attribute values, and present and evaluate three new methods for recovering or discovering such orders, showing that under some circumstances they can significantly improve accuracy. In addition we introduce the use of classifier ensembles that use random value orders as a source of variation, and show that this can also lead to significant accuracy gains.