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An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Susmita Datta1, Vasyl Pihur, Somnath Datta
1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.
BMC Bioinformatics
|August 19, 2010
Summary
This study introduces an adaptive ensemble classifier that combines bagging and rank aggregation. This novel approach improves predictive performance by adaptively optimizing classification for various data types and multiple performance measures.
Area of Science:
- Machine Learning
- Computational Biology
- Data Science
Background:
- Selecting optimal classification algorithms is challenging due to data-dependent performance.
- Classifier performance can vary based on the chosen evaluation metric.
- A priori selection of a single best algorithm is often impractical.
Purpose of the Study:
- To develop a novel adaptive ensemble classifier.
- To create a classifier capable of optimizing performance across multiple metrics simultaneously.
- To improve predictive performance compared to naive approaches.
Main Methods:
- Combining bagging and rank aggregation techniques.
- Developing a multi-objective optimization framework for classification.
- Adaptive adjustment of the ensemble based on data characteristics.
Main Results:
- The proposed ensemble classifier demonstrated robust performance across simulated and real-world datasets.
- In all tested cases, the ensemble classifier matched or exceeded the performance of the best individual classifier.
- The adaptive strategy outperformed a naive approach relying solely on training data performance.
Conclusions:
- For complex, high-dimensional datasets, ensemble methods are recommended over fixed algorithm selections.
- Combining multiple classification algorithms with dimension reduction techniques is a promising strategy.
- Adaptive ensemble classifiers offer superior performance for diverse data types.
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