Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Second Order Cone Programming Formulations for Feature Selection
The Journal of Machine Learning Research
LESS: A Model-Based Classifier for Sparse Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Feature selection of radar-derived attributes with linear programming support vector machines
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Liknon Feature Selection for Microarrays
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Component-based discriminative classification for hidden Markov models
Pattern Recognition
Identification of signatures in biomedical spectra using domain knowledge
Artificial Intelligence in Medicine
IEEE Transactions on Image Processing
A sparse nearest mean classifier for high dimensional multi-class problems
Pattern Recognition Letters
Variable selection and prediction of rainfall from WSR-88D radar using support vector regression
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Robustness analysis of eleven linear classifiers in extremely high–dimensional feature spaces
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
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Molecular profiling technologies monitor many thousands of transcripts, proteins, metabolites or other species concurrently in a biological sample of interest. Given such high-dimensional data for different types of samples, classification methods aim to assign specimens to known categories. Relevant feature identification methods seek to define a subset of molecules that differentiate the samples. This work describes LIKNON, a specific implementation of a statistical approach for creating a classifier and identifying a small number of relevant features simultaneously. Given two-class data, LIKNON estimates a sparse linear classifier by exploiting the simple and well-known property that minimising an L1 norm (via linear programming) yields a sparse hyperplane. It performs well when used for retrospective analysis of three cancer biology profiling data sets, (i) small, round, blue cell tumour transcript profiles from tumour biopsies and cell lines, (ii) sporadic breast carcinoma transcript profiles from patients with distant metastases