Learning and classification of monotonic ordinal concepts
Computational Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Adding monotonicity to learning algorithms may impair their accuracy
Expert Systems with Applications: An International Journal
Monotone Relabeling in Ordinal Classification
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Feature Selection for Monotonic Classification
IEEE Transactions on Fuzzy Systems
Rank Entropy-Based Decision Trees for Monotonic Classification
IEEE Transactions on Knowledge and Data Engineering
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In this paper we focus on rank discrimination measures, i.e., functions able to quantify the discrimination power of an attribute w.r.t. the class, taking into account the monotonicity of the class w.r.t. the attribute. These measures are used in decision tree induction in order to enforce a local form of monotonicity of the class w.r.t. the splitting attribute and are characterized by a noticeable robustness to non-monotone noise present in the data. More precisely, here we present a hierarchical model in order to single out which properties a function must satisfy to be a rank discrimination measure, providing in this way a framework for the construction of new measures.