A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Leveraging the margin more carefully
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
A new efficient algorithm based on DC programming and DCA for clustering
Journal of Global Optimization
Support vector machines with adaptive Lq penalty
Computational Statistics & Data Analysis
Sparse signal recovery by difference of convex functions algorithms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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Gene selection is a very important problem in microarray data analysis and has critical implications for the discovery of genes related to serious diseases. In this paper the problem of gene selection for cancer classification is considered. We develop a combined SVMs - feature selection approach based on the Smoothly Clipped Absolute Deviation penalty, minimizing directly the classifier performance. To solve our optimization problems, we apply the DCA (Difference of Convex functions Algorithms) which is a general framework for non-convex continuous optimization. This leads to a successive linear programming algorithm with finite convergence. Preliminary computational experiments on different real data demonstrate that our methods accomplish the desired goal: suppression of a large number of features with a small error of classification.