2008 ANewApproachtoImprovetheVoteBas

• (Parvin et al., 2008) ⇒ Hamid Parvin, Hosein Alizadeh, and Behrouz Minaei-Bidgoli. (2008). “A New Approach to Improve the Vote-Based Classifier Selection.” In: Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 02. doi:10.1109/NCM.2008.229

Subject Headings: Weighted Vote-Based Classifier.

Cited By

• http://scholar.google.com/scholar?q=%22A+New+Approach+to+Improve+the+Vote-Based+Classifier+Selection%22+2008
• http://dl.acm.org/citation.cfm?id=1443229.1444220&preflayout=flat#citedby

Quotes

Abstract

In the past decade many new methods were proposed for combining multiple classifiers. Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. We propose a GA-based method for constructing a neural network ensemble using a weighted vote-based classifier selection approach. Main presumption of this method is that the reliability of the predictions of each classifier differs among classes. During testing, the classifiers whose votes are considered as being reliable are combined using weighted majority voting. This method of combination outperforms the ensemble of all classifiers almost 2.26% and 4.00% on Hoda and Wine data sets, respectively.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ANewApproachtoImprovetheVoteBas	Hamid Parvin Hosein Alizadeh Behrouz Minaei-Bidgoli			A New Approach to Improve the Vote-Based Classifier Selection				10.1109/NCM.2008.229