The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Supervised subspace projections for constructing ensembles of classifiers
Information Sciences: an International Journal
On the effect of calibration in classifier combination
Applied Intelligence
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This paper presents an experimental study using different projection strategies and techniques to improve the performance of Support Vector Machine (SVM) ensembles. The study has been made over 62 UCI datasets using Principal Component Analysis (PCA) and three types of Random Projections (RP), taking into account the size of the projected space and using linear SVMs as base classifiers. Random Projections are also combined with the sparse matrix strategy used by Rotation Forests, which is a method based in projections too. Experiments show that for SVMs ensembles (i) sparse matrix strategy leads to the best results, (ii) results improve when projected space dimension is bigger than the original one, and (iii) Random Projections also contribute to the results enhancement when used instead of PCA. Finally, random projected SVMs are tested as base classifiers of some state of the art ensembles, improving their performance.