Entropy criterion for classifier-independent feature selection

Authors:
Naoto Abe;Mineichi Kudo
Affiliations:
Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 6
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
The nature of statistical learning theory

The nature of statistical learning theory
Divergence Based Feature Selection for Multimodal Class Densities

IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
SVMTorch: support vector machines for large-scale regression problems

The Journal of Machine Learning Research
A Recursive Partitioning Decision Rule for Nonparametric Classification

IEEE Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature selection aims to select a feature subset that has discriminative information from the original feature set. In practice, we do not know what classifier is used beforehand, and it is preferable to find a feature subset that is universally effective for any classifier. Such a trial is called classifier-independent feature selection and can be made by removing garbage features that have no discriminative information. However, it is difficult to distinguish only garbage features from the others. In this study, we propose an entropy criterion for this goal and confirm the effectiveness through a synthetic dataset.