Abstract
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.1155/2015/909123
Rights Information
© 2015 The authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication Date
2015
Recommended Citation
Nanni, Loris, Sheryl Brahnam, Stefano Ghidoni, and Alessandra Lumini. "Toward a General-Purpose heterogeneous ensemble for pattern classification." Computational intelligence and neuroscience 2015 (2015): 85.
Journal Title
Computational intelligence and neuroscience
