Forward Gene Selection Algorithm Based on Least Squares Support Vector Machine
DNA microarrays could provide useful information for cancer classification at gene expression level. The number of genes in a microarray is always several thousand while the number of training samples is always less than one hundred. In such case overfitting is a serious shortcoming for the machine learning algorithms. It is necessary to select the most informative genes to construct the input vectors of machine learning algorithms. A gene selection algorithm based on leave-one-out cross validation and least squares support vector machines is proposed in this paper. The proposed algorithm adopts sequential forward selection search scheme and can be used to determine adaptively the number of selected genes. The linear kernel function and the polynomial kernel function are used for least squares support vector machine. Results of numerical experiments show that the proposed algorithm is effective and comparable performance is achieved on six well-known benchmark problems.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: August 1, 2013
More about this publication?
- Bionanoscience attempts to harness various functions of biological macromolecules and integrate them with engineering for technological applications. It is based on a bottom-up approach and encompasses structural biology, biomacromolecular engineering, material science, and engineering, extending the horizon of material science. The journal aims at publication of (i) Letters (ii) Reviews (3) Concepts (4) Rapid communications (5) Research papers (6) Book reviews (7) Conference announcements in the interface between chemistry, physics, biology, material science, and technology. The use of biological macromolecules as sensors, biomaterials, information storage devices, biomolecular arrays, molecular machines is significantly increasing. The traditional disciplines of chemistry, physics, and biology are overlapping and coalescing with nanoscale science and technology. Currently research in this area is scattered in different journals and this journal seeks to bring them under a single umbrella to ensure highest quality peer-reviewed research for rapid dissemination in areas that are in the forefront of science and technology which is witnessing phenomenal and accelerated growth.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites