The research of feature selection model based on the game theory

Authors:
Maixia Fu;Yuan Zhang;Feiyu Lian
Affiliations:
School of Information Science and Technology, Henan University of Technology, Zhengzhou, Henan, China and School of Information and Engineering, Zhengzhou University, Zhengzhou, Henan, China;School of Information Science and Technology, Henan University of Technology, Zhengzhou, Henan, China;School of Information Science and Technology, Henan University of Technology, Zhengzhou, Henan, China
Venue:
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Year:
2009

Citing 6
Cited 0

A note on genetic algorithms for large-scale feature selection

Pattern Recognition Letters
A practical approach to feature selection

ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Unsupervised Feature Selection Using Feature Similarity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Performance in Neural Networks Using a Boosting Algorithm

Advances in Neural Information Processing Systems 5, [NIPS Conference]
An introduction to variable and feature selection

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper depicts the uncertainty of feature information by using mutual information entrop based on the game theory. It also exposes the underlying nature of the decision-making process that is founded on conflict and cooperation through the construction of a payment function. Finally, the paper seeks to provide a balance solution for this strategy by the payoff matrix so that the optimum feature subset can be obtained. This feature subset can improve the performance of the decision-making system; hence, the feature dimension catastrophe caused by the high feature dimension can be subsequently addressed. Consequently, after the above-mentioned methods have been used to conduct feature selection as applied in vehicle selection, it may stated that vehicle feature dimensionality can be compressed. The recognition efficiency can also be improved greatly.