Oblique decision tree induction by cross-entropy optimization based on the von Mises-Fisher distribution

DOI10.1007/s00180-022-01195-7zbMath1505.62073OpenAlexW4210421466MaRDI QIDQ2095749

Stephan Westphal, Ferdinand Bollwein

Publication date: 15 November 2022

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-022-01195-7

zbMATH Keywords

classificationvon Mises-Fisher distributionregressioncross-entropy optimizationoblique decision trees

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• UCI-ml
• Scikit
• HHCART

Cites Work

• Model-based search for combinatorial optimization: A critical survey
• Optimization of computer simulation models with rare events
• HHCART: an oblique decision tree
• The cross-entropy method for combinatorial and continuous optimization
• Sparsity in optimal randomized classification trees
• A tutorial on the cross-entropy method
• A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of \(I_{s}(x)\)
• Computer Generation of Distributions on the m-Sphere
• Simulation of the von mises fisher distribution
• Enumeration of Seven-Argument Threshold Functions
• Dispersion on a Sphere
• A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
• Optimal classification trees
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