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Local classifier weighting by quadratic programming.

Hakan Cevikalp¹, Robi Polikar

¹Electrical and Electronics Engineering Department, Eskisehir Osmangazi University, Meselik, Eskisehir, Turkey. hakan.cevikalp@gmail.com

IEEE Transactions on Neural Networks

|October 10, 2008

Summary

This summary is machine-generated.

Combining multiple classifiers improves accuracy. This study introduces a dynamic approach using local accuracy estimates to weight classifiers, outperforming existing methods, especially for complex data.

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Area of Science:

Machine Learning
Pattern Recognition
Artificial Intelligence

Background:

Combining multiple classifiers enhances classification accuracy.
Heuristic methods exist for combining diverse classifier outputs, but optimal strategies remain an open problem.
Classifiers often specialize in different input space regions.

Purpose of the Study:

To develop a dynamic approach for combining classifiers with regional expertise.
To improve classification accuracy by adaptively weighting classifier outputs based on local performance.

Main Methods:

Estimating local classifier accuracies using nearest neighbors of a query sample.
Formulating classifier weighting as a convex quadratic optimization problem.
Assigning optimal nonnegative weights to classifiers based on local accuracy estimates.

Main Results:

The proposed dynamic weighting scheme significantly outperforms popular classifier combination methods.
The method demonstrates superior performance on datasets with complex decision boundaries.
Locally accurate classifiers are weighted more heavily, leading to improved query labeling.

Conclusions:

Local classification-accuracy-based combination is effective for diverse classifier ensembles.
This dynamic approach offers a robust solution for combining classifiers with specialized knowledge.
The method is well-suited for decision-making in complex pattern recognition tasks.