Robust cutpoints in the logical analysis of numerical data

Martin Anthony; Joel Ratsaby

doi:https://doi.org/10.1016/j.dam.2011.07.014

Robust cutpoints in the logical analysis of numerical data

Martin Anthony, Joel Ratsaby

Department of Electrical and Electronics Engineering

Ariel University

Research output: Contribution to journal › Article › peer-review

Abstract

Techniques for the logical analysis of binary data have successfully been applied to non-binary data which has been 'binarized' by means of cutpoints; see Boros et al. (1997, 2000) [7,8]. In this paper, we analyze the predictive performance of such techniques and, in particular, we derive generalization error bounds that depend on how 'robust' the cutpoints are.

Original language	American English
Pages (from-to)	355-364
Number of pages	10
Journal	Discrete Applied Mathematics
Volume	160
Issue number	4-5
DOIs	https://doi.org/10.1016/j.dam.2011.07.014
State	Published - 1 Mar 2012

Keywords

Generalization error
LAD methods
Learning algorithms
Logical analysis of data
Machine learning

All Science Journal Classification (ASJC) codes

Discrete Mathematics and Combinatorics
Applied Mathematics

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https://doi.org/10.1016/j.dam.2011.07.014

Cite this

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KW - Learning algorithms

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Robust cutpoints in the logical analysis of numerical data

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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