@inproceedings{223fe4f3205948e8a96203fec42a095e,
title = "Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy",
abstract = "Recursive feature elimination based on non-linear kernel support vector machine (SVM-RFE) with parameter selection by genetic algorithm is an effective algorithm to perform gene selection and cancer classification in some degree, but its calculating complexity is too high for implementation. In this paper, we propose a new strategy to use adaptive kernel parameters in the recursive feature elimination algorithm implemented with Gaussian kernel SVMs as a better alternatives to the aforementioned algorithm for pragmatic reasons. The proposed method performs well in selecting genes and achieves high classification accuracies with these genes on two cancer datasets.",
keywords = "Feature selection, Machine learning, Recursive feature elimination, Support vector machine",
author = "Yong Mao and Xiaobo Zhou and Zheng Yin and Daoying Pi and Youxian Sun and Wong, {Stephen T C}",
year = "2006",
doi = "10.1007/11795131_116",
language = "English (US)",
isbn = "3540362975",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "799--806",
booktitle = "Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings",
note = "First International Conference on Rough Sets and Knowledge Technology, RSKT 2006 ; Conference date: 24-07-2006 Through 26-07-2006",
}