Gene extraction for cancer diagnosis by support vector machines

Authors:
Te-Ming Huang;Vojislav Kecman
Affiliations:
School of Engineering, The University of Auckland, New Zealand;School of Engineering, The University of Auckland, New Zealand
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

Citing 3
Cited 2

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Variable selection using svm based criteria

The Journal of Machine Learning Research

Liknon Feature Selection for Microarrays

WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Short text authorship attribution via sequence kernels, Markov chains and author unmasking: an investigation

EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A cancer diagnosis by using the DNA microarray data faces many challenges the most serious one being the presence of thousands of genes and only several dozens (at the best) of patient’s samples. Thus, making any kind of classification in high-dimensional spaces from a limited number of data is both an extremely difficult and a prone to an error procedure. The improved Recursive Feature Elimination with Support Vector Machines (RFE-SVMs) is introduced and used here for an elimination of less relevant genes and just for a reduction of the overall number of genes used in a medical diagnostic. The paper shows why and how the, usually neglected, penalty parameter C influence classification results and the gene selection of RFE-SVMs. With an appropriate parameter C chosen, the reduction in a diagnosis error is as high as 37% on the colon cancer data set. The results suggest that with a properly chosen parameter C, the extracted genes and the constructed classifier will ensure less over-fitting of the training data leading to an increase accuracy in selecting relevant genes.