The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Classification of DNA microarray data with Random Projection Ensembles of Polynomial SVMs
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Wavelet selection for disease classification by DNA microarray data
Expert Systems with Applications: An International Journal
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm
Knowledge-Based Systems
Target detection based on a dynamic subspace
Pattern Recognition
Random subspace support vector machine ensemble for reliable face recognition
International Journal of Biometrics
A high-dimensional two-sample test for the mean using random subspaces
Computational Statistics & Data Analysis
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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Support vector machines (SVMs), and other supervised learning techniques have been experimented for the bio-molecular diagnosis of malignancies, using also feature selection methods. The classification task is particularly difficult because of the high dimensionality and low cardinality of gene expression data. In this paper we investigate a different approach based on random subspace ensembles of SVMs: a set of base learners is trained and aggregated using subsets of features randomly drawn from the available DNA microarray data. Experimental results on the colon adenocarcinoma diagnosis and medulloblastoma clinical outcome prediction show the effectiveness of the proposed approach.