Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
Dualization, decision lists and identification of monotone discrete functions
Annals of Mathematics and Artificial Intelligence
Logical analysis of data with decomposable structures
Theoretical Computer Science
Prior Knowledge in Economic Applications of Data Mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Rough Sets and Ordinal Classification
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
A Decision Tree Algorithm for Ordinal Classification
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
The Knowledge Engineering Review
Decision trees for ordinal classification
Intelligent Data Analysis
Comparison of classification accuracy using Cohen's Weighted Kappa
Expert Systems with Applications: An International Journal
A linear fit gets the correct monotonicity directions
Machine Learning
Rule effectiveness in rule-based systems: A credit scoring case study
Expert Systems with Applications: An International Journal
On the random generation of monotone data sets
Information Processing Letters
Monotone Mamdani--Assilian models under mean of maxima defuzzification
Fuzzy Sets and Systems
Nearest Neighbour Classification with Monotonicity Constraints
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Adding monotonicity to learning algorithms may impair their accuracy
Expert Systems with Applications: An International Journal
Two algorithms for generating structured and unstructured monotone ordinal data sets
Engineering Applications of Artificial Intelligence
Rule learning with monotonicity constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Fundamenta Informaticae - Fundamentals of Knowledge Technology
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
IEEE Transactions on Fuzzy Systems
On the monotonicity of fuzzy-inference methods related to T-S inference method
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Learnability in rough set approaches
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
Learning monotone nonlinear models using the choquet integral
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Monotone instance ranking with MIRA
DS'11 Proceedings of the 14th international conference on Discovery science
Ant-based approach to the knowledge fusion problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
Performance of classification models from a user perspective
Decision Support Systems
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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Decision trees that are based on information-theory are useful paradigms for learning from examples. However, in some real-world applications, known information-theoretic methods frequently generate nonmonotonic decision trees, in which objects with better attribute values are sometimes classified to lower classes than objects with inferior values. This property is undesirable for problem solving in many application domains, such as credit scoring and insurance premium determination, where monotonicity of subsequent classifications is important. An attribute-selection metric is proposed here that takes both the error as well as monotonicity into account while building decision trees. The metric is empirically shown capable of significantly reducing the degree of non-monotonicity of decision trees without sacrificing their inductive accuracy.