Structure and interpretation of computer programs
Structure and interpretation of computer programs
On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Concurrent Discretization of Multiple Attributes
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Typicality, Diversity, and Feature Pattern of an Ensemble
IEEE Transactions on Computers
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This paper lies within the domain of supervised discretization methods. The methodology aims at identifying relevant interactions between input and output variables. A new supervised discretization algorithm that takes into account the qualitative ordinal structure of the output variable is proposed. Most existing supervised discretization methods are designed for pattern recognition problems and do not take into account this ordinal structure. A qualitative distance is constructed over the discrete structure of absolute orders of magnitude spaces. The algorithm presented implements a maximization process of this distance. A simple example allows interpretation of the process of choosing landmarks.