Limitations on Low Variance k-Fold Cross Validation in Learning Set of Rules Inducers

Limitations on Low Variance k-Fold Cross Validation in Learning Set of Rules Inducers

One of the standard methods in a verification of predictive models is a cross validation. In this paper, we examined prediction stability of simple learning set of rules classifier under the k-fold cross validation. We described a class of rules that can pass the k-fold cross validation with zero or...

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Bibliographic Details
Published in:2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS) pp. 207 - 214
Main Authors: Vasinek, Michal, Plato, Jan, Snasel, Vaclav
Format: Conference Proceeding
Language:English
Published: IEEE 01-09-2016
Subjects:
Computer science
Estimation
Prediction algorithms
Predictive models
Silicon
Stability analysis
Training
Online Access:Get full text
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Summary:One of the standard methods in a verification of predictive models is a cross validation. In this paper, we examined prediction stability of simple learning set of rules classifier under the k-fold cross validation. We described a class of rules that can pass the k-fold cross validation with zero or a very low variance in accuracy of prediction. The lossless prediction of correct/incorrect assignment distribution theorem, given by the so-called k-fold stable rules, is established, and its implications are discussed and applied in the experiments.
DOI:10.1109/INCoS.2016.51