A \(k\)-norm pruning algorithm for decision tree classifiers based on error rate estimation

DOI10.1007/s10994-007-5044-4zbMath1470.62100OpenAlexW2141828203MaRDI QIDQ1009253

Mingyu Zhong, Michael Georgiopoulos, Georgios C. Anagnostopoulos

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-007-5044-4

zbMATH Keywords

pruningdecision treelaw of succession

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (1)

Integrated Fisher linear discriminants: an empirical study

Uses Software

• UCI-ml
• C4.5

Cites Work

• Cross-validation:a review²
• Laplace's law of succession and universal encoding
• Probability Inequalities for Sums of Bounded Random Variables
• A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
• Approximate match of rules using backpropagation neural networks
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