Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
The Journal of Machine Learning Research
Effectiveness of Rotation Forest in Meta-learning Based Gene Expression Classification
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
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
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The classification of genomic and proteomic data in extremely high dimensional datasets is a well-known problem which requires appropriate classification techniques. Classification methods are usually combined with gene selection techniques to provide optimal classification conditions-i.e. a lower dimensional classification environment. Another reason for reducing the dimensionality of such datasets is their interpretability, as it is much easier to interpret a small set of ranked genes than 20 thousand genes. This paper evaluates the classification performance of Rotation Forest classifier on small subsets of ranked genes for two dataset collections consisting of 47 genomic and proteomic classification problems. Robustness and high classification accuracy is shown to be an important feature of Rotation Forest when applied to small sets of genes.