Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Pattern Discovery by Residual Analysis and Recursive Partitioning
IEEE Transactions on Knowledge and Data Engineering
Design of Hierarchical Classifiers
Proceedings of the The First Great Lakes Computer Science Conference on Computing in the 90's
On maximum entropy discretization and its applications in pattern recognition
On maximum entropy discretization and its applications in pattern recognition
Transparent Decision Support Using Statistical Reasoning and Fuzzy Inference
IEEE Transactions on Knowledge and Data Engineering
A decision support framework for clinical needle EMG
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
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The marginal maximum entropy criterion has been used to guide recursive partitioning of a continuous sample space. Although the criterion has been successfully applied in pattern discovery applications, its theoretical justification has not been clearly addressed. In this paper, it is shown that the basic marginal maximum entropy partitioning algorithm yields asymptotically consistent density estimates. This result supports the use of the marginal maximum entropy criterion in pattern discovery and implies that an optimal classifier can be constructed.