A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
Machine Learning
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on 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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Selecting few genes for microarray gene expression classification
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Rotation forest on microarray domain: PCA versus ICA
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Expert Systems with Applications: An International Journal
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Random Forests, Support Vector Machines and k-Nearest Neighbors are successful and proven classification techniques that are widely used for different kinds of classification problems. One of them is classification of genomic and proteomic data that is known as a problem with extremely high dimensionality and therefore demands suited classification techniques. In this domain they are usually combined with gene selection techniques to provide optimal classification accuracy rates. Another reason for reducing the dimensionality of such datasets is their interpretability. It is much easier to interpret a small set of ranked genes than 20 or 30 thousands of unordered genes. In this paper we present a classification ensemble of decision trees called Rotation Forest and evaluate its classification performance on small subsets of ranked genes for 14 genomic and proteomic classification problems. An important feature of Rotation Forest is demonstrated --- i.e. robustness and high classification accuracy using small sets of genes.