Local within-class accuracies for weighting individual outputs in multiple classifier systems

Authors:
Shiliang Sun
Affiliations:
Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China
Venue:
Pattern Recognition Letters
Year:
2010

Citing 11
Cited 2

Decision Combination in Multiple Classifier Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging predictors

Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates

IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
An experimental evaluation of ensemble methods for EEG signal classification

Pattern Recognition Letters
The random electrode selection ensemble for EEG signal classification

Pattern Recognition
An improved random subspace method and its application to EEG signal classification

MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Using random subspace to combine multiple features for face recognition

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition

A centroid k-nearest neighbor method

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Multi-metric learning for multi-sensor fusion based classification

Information Fusion

Quantified Score

Hi-index	0.11

Visualization

Abstract

A new method for assigning weights to individual classifiers in a multiple classifier system based on their local within-class accuracies is proposed. First distance metric learning is applied to determine the within-class nearest neighbors for an example to be classified. Then the local within-class accuracy for an individual classifier while classifying this example is judged by its performance on these neighbors. Experiments on a number of data sets with comparisons to two other existing methods show the effectiveness of the proposed method. Practical considerations about its applicability and asymptotic behavior analysis for theoretical justification are also provided.