Knowledge evaluation: Other evaluations: minimum description length

Authors:
Alexander Tuzhilin
Affiliations:
Associate Professor of Information Systems, Stern School of Business, New York University, New York
Venue:
Handbook of data mining and knowledge discovery
Year:
2002

Citing 8
Cited 1

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Inferring decision trees using the minimum description length principle

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Abstract

This article describes the MDL principle that selects the model minimizing the total number of bits needed to encode the model and the data given the model. The article also explores the connection of the MDL principle to the maximum a posteriori (MAP) hypothesis and the Occam's razor principle. Finally, it describes how the MDL principle is applied to the decision tree pruning problem.