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Open Access Methodology article

An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data

Susmita Datta1, Vasyl Pihur2 and Somnath Datta1*

Author Affiliations

1 Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA

2 Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA

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BMC Bioinformatics 2010, 11:427  doi:10.1186/1471-2105-11-427

Published: 18 August 2010

Abstract

Background

Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show that our somewhat complex strategy has better predictive performance as judged on test samples than a more naive approach that attempts to directly identify the optimal classifier based on the training data performances of the individual classifiers.

Results

We illustrate the proposed method with two simulated and two real-data examples. In all cases, the ensemble classifier performs at the level of the best individual classifier comprising the ensemble or better.

Conclusions

For complex high-dimensional datasets resulting from present day high-throughput experiments, it may be wise to consider a number of classification algorithms combined with dimension reduction techniques rather than a fixed standard algorithm set a priori.