Information theory and classification error in probabilistic classifiers

Authors:
Aritz Páerez;Pedro Larrañaga;Iñaki Inza
Affiliations:
Intelligent Systems Group, Computer Science and A.I Dept., University of The Basque Country, Spain;Intelligent Systems Group, Computer Science and A.I Dept., University of The Basque Country, Spain;Intelligent Systems Group, Computer Science and A.I Dept., University of The Basque Country, Spain
Venue:
DS'06 Proceedings of the 9th international conference on Discovery Science
Year:
2006

Citing 1
Cited 3

Elements of information theory

Elements of information theory

Discriminative vs. Generative Learning of Bayesian Network Classifiers

ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Bayesian classifiers based on kernel density estimation: Flexible classifiers

International Journal of Approximate Reasoning
Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

Environmental Modelling & Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work shows, using bivariate continuous artificial domains, the relation that seems to exist between some measures based on the information theory and the expected classification error. The relations that seem to be found in this work could be applied to the improvement of the classifiers which assign a posteriori probabilities to each class value. They also could be used in other tasks related to the supervised classification such as feature subset selection or discretization.