A framework for the analysis of majority voting

Authors:
Anand M. Narasimhamurthy
Affiliations:
Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA
Venue:
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Year:
2003

Citing 6
Cited 3

Decision Combination in Multiple Classifier Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies

IEEE Transactions on Pattern Analysis and Machine Intelligence
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
Critic-driven ensemble classification

IEEE Transactions on Signal Processing
Application of majority voting to pattern recognition: an analysis of its behavior and performance

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Theoretical Bounds of Majority Voting Performance for a Binary Classification Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using genetic programming to classify node positive patients in bladder cancer

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evaluation of diversity measures for binary classifier ensembles

MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Majority voting is a very popular combination scheme both because of its simplicity and its performance on real data. A number of earlier studies have attempted a theoretical analysis of majority voting. Many of them assume independence of the classifiers while deriving analytical expressions.We propose a framework which does not incorporate any assumptions. For a binary classification problem, given the accuracies of the classifiers in the team, the theoretical upper and lower bounds for performance obtained by combining them through majority voting are shown to be solutions of a linear programming problem. The framework is general and could provide insight into majority voting.