Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
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
Signal Processing - Special issue: Genomic signal processing
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
IEEE Transactions on Image Processing
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
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Molecular profiling technologies monitor thousands of transcripts, proteins, metabolites or other species concurrently in biological samples of interest. Given two-class, high-dimensional profiling data, nominal Liknon [4] is a specific implementation of a methodology for performing simultaneous relevant feature identification and classification. It exploits the well-known property that minimizing an l1 norm (via linear programming) yields a sparse hyperplane [15,26,2,8,17]. This work (i) examines computational, software and practical issues required to realize nominal Liknon, (ii) summarizes results from its application to five real world data sets, (iii) outlines heuristic solutions to problems posed by domain experts when interpreting the results and (iv) defines some future directions of the research.