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
LS Bound based gene selection for DNA microarray data
Bioinformatics
Microarray data classification based on ensemble independent component selection
Computers in Biology and Medicine
A fast multi-output RBF neural network construction method
Neurocomputing
Gene selection and classification using Taguchi chaotic binary particle swarm optimization
Expert Systems with Applications: An International Journal
Stable Gene Selection from Microarray Data via Sample Weighting
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Sparse Linear Regression With Structured Priors and Application to Denoising of Musical Audio
IEEE Transactions on Audio, Speech, and Language Processing
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
The small sample size problem of ICA: A comparative study and analysis
Pattern Recognition
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This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L"2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches.