Stratified Cross-Validation on Multiple Columns

Stratified Cross-Validation on Multiple Columns

Stratified cross-validation is one of the standard methods of how to evaluate classifier's generalization accuracy. However, conventional implementations of cross-validation can stratify only by a single column. In this paper, we propose to utilize Integer Linear Programming in order to enable...

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Bibliographic Details
Published in:2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) pp. 26 - 31
Main Authors: Motl, Jan, Kordik, Pavel
Format: Conference Proceeding
Language:English
Published: IEEE 01-11-2021
Subjects:
Artificial intelligence
Conferences
Cross-validation
Integer linear programming
Measurement
Multi-label classification
Partitioning
Testing
Online Access:Get full text
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Summary:Stratified cross-validation is one of the standard methods of how to evaluate classifier's generalization accuracy. However, conventional implementations of cross-validation can stratify only by a single column. In this paper, we propose to utilize Integer Linear Programming in order to enable stratification by multiple columns. Our experiments using an extensive set of multi-label data sets shows that the proposed method significantly outperforms non-stratified cross-validation.
ISSN:2375-0197
DOI:10.1109/ICTAI52525.2021.00012