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An analysis of ensemble pruning techniques based on ordered aggregation.

Gonzalo Martínez-Muñoz¹, Daniel Hernández-Lobato, Alberto Suárez

¹Computer Science Department, Universidad Autónoma de Madrid, Cantoblanco, Spain. gonzalo.martinez@uam.es

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 27, 2008

Summary

This study introduces ordered aggregation for pruning bagging ensembles, enhancing accuracy and reducing size. This method efficiently creates competitive pruned ensembles, outperforming complex subensemble selection techniques.

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Area of Science:

Machine Learning
Ensemble Methods

Background:

Bagging ensembles can be computationally expensive and may not always yield optimal accuracy.
Current pruning strategies often involve complex subensemble selection.

Purpose of the Study:

To analyze pruning strategies for reducing bagging ensemble size and improving accuracy.
To investigate the impact of classifier aggregation order on ensemble performance.

Main Methods:

Modifying the order of classifier aggregation in bagging ensembles.
Developing heuristics to select subsets of complementary classifiers.
Evaluating pruned ensembles on benchmark classification tasks.

Main Results:

Ordered aggregation can lead to a minimum generalization error at intermediate ensemble sizes, below the asymptotic error of standard bagging.
Pruned ensembles generated via ordered aggregation demonstrate competitive performance and robustness.
This approach is more efficient than methods that directly select optimal subensembles.

Conclusions:

Ordered aggregation is an efficient strategy for generating pruned bagging ensembles.
Pruned ensembles created through this method offer comparable performance to more computationally intensive techniques.
This research provides a novel approach to optimize ensemble learning models.