Split Criterions for Variable Selection Using Decision Trees

Authors:
Joaquín Abellán;Andrés R. Masegosa
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Spain
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

Citing 9
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Induction of Decision Trees

Machine Learning
Inference for the Generalization Error

Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining

IEEE Transactions on Knowledge and Data Engineering
Maximum of entropy for credal sets

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Uncertainty and Information: Foundations of Generalized Information Theory

Uncertainty and Information: Foundations of Generalized Information Theory
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Upper entropy of credal sets. Applications to credal classification

International Journal of Approximate Reasoning
An introduction to the imprecise Dirichlet model for multinomial data

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the field of attribute mining, several feature selection methods have recently appeared indicating that the use of sets of decision trees learnt from a data set can be an useful tool for selecting relevant and informative variables regarding to a main class variable. With this aim, in this study, we claim that the use of a new split criterion to build decision trees outperforms another classic split criterions for variable selection purposes. We present an experimental study on a wide and different set of databases using only one decision tree with each split criterion to select variables for the Naive Bayes classifier.