Comparing multi-class classifiers: on the similarity of confusion matrices for predictive toxicology applications

Authors:
Mokhairi Makhtar;Daniel C. Neagu;Mick J. Ridley
Affiliations:
School of Computing, Informatics and Media, University of Bradford, Bradford, UK;School of Computing, Informatics and Media, University of Bradford, Bradford, UK;School of Computing, Informatics and Media, University of Bradford, Bradford, UK
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

Citing 4
Cited 0

Glossary of Terms

Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Confusion matrix disagreement for multiple classifiers

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

Pattern Recognition
Binary classification models comparison: on the similarity of datasets and confusion matrix for predictive toxicology applications

ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics

Quantified Score

Hi-index	0.01

Visualization

Abstract

Calculating the similarity of predictive models helps to characterize the models diversity and to identify relevant models from a collection of models. The relevant models are considered based on their performance, calculated using their confusion matrix. In this paper, we propose a methodology to measure the similarity for predictive models performances by comparing their confusion matrices. In this research, we focus on multi-class classifiers for toxicology applications. The performance measures of confusion matrices of multi-class classifiers are regrouped into a binary classification problem. Such approach may result in selecting multi-class classifiers with lower False Negative Rate (FNR) for example. Consequently, the methodology for model comparison based on the similarity of confusion matrices provides a working way to select models from a collection of classifiers.