Dynamic and static weighting in classifier fusion

Authors:
Rosa M. Valdovinos;J. Salvador Sánchez;Ricardo Barandela
Affiliations:
Instituto Tecnológico de Toluca, Metepec, México;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló, Spain;Instituto Tecnológico de Toluca, Metepec, México
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

Citing 7
Cited 5

Bagging predictors

Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft combination of neural classifiers: a comparative study

Pattern Recognition Letters
Generating classifier outputs of fixed accuracy and diversity

Pattern Recognition Letters
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Voting Nearest-Neighbor Subclassifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning

On Combining Classifiers by Relaxation for Natural Textures in Images

HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
On combining classifiers through a fuzzy multicriteria decision making approach: Applied to natural textured images

Expert Systems with Applications: An International Journal
A Hopfield Neural Network for combining classifiers applied to textured images

Neural Networks
Performance analysis of fuzzy aggregation operations for combining classifiers for natural textures in images

HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

When a Multiple Classifier System is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting is affected negatively. In this paper, a comparison between simple and weighted voting (both dynamic and static) is presented. New weighting methods, mainly in the direction of the dynamic approach, are also introduced. Experimental results with several real-problem data sets demonstrate the advantages of the weighting strategies over the simple voting scheme. When comparing the dynamic and the static approaches, results show that the dynamic weighting is superior to the static strategy in terms of classification accuracy.