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
Independent component analysis: algorithms and applications
Neural Networks
Inference for the Generalization Error
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A review of feature selection techniques in bioinformatics
Bioinformatics
Cancer classification using Rotation Forest
Computers in Biology and Medicine
Feature Selection and Classification for Small Gene Sets
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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Rotation Forest (RF) is an ensemble method that has shown effectiveness on microarray data set classification problems. RF works by generating sparse rotation matrixes of the input space, a method that creates accurate and diverse base classifiers. In its original formulation, elemental rotations were obtained by Principal Component Analysis (PCA). However, for microarray data sets, Independent Component Analysis (ICA) may be a better option. In this paper, an experimental study on ten microarray data sets has been performed. The study confirms that, except for a small number of attributes, Rotation Forest outperforms Bagging and Boosting on this domain. However, RF with ICA does not generally improve on RF with PCA.