Feature selection for nonlinear regression and its application to cancer research

Yijun Sun, Jin Yao, Steven Goodison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Feature selection is a fundamental problem in machine learning. With the advent of high-throughput technologies, it becomes increasingly important in a wide range of scientific disciplines. In this paper, we consider the problem of feature selection for high-dimensional nonlinear regression. This problem has not yet been well addressed in the community, and existing methods suffer from issues such as local minima, simplified model assumptions, high computational complexity and selected features not directly related to learning accuracy. We propose a new wrapper method that addresses some of these issues. We start by developing a new approach to estimating sample responses and prediction errors, and then deploy a feature weighting strategy to find a feature subspace where a prediction error function is minimized. We formulate it as an optimization problem within the SVM framework and solve it using an iterative approach. In each iteration, a gradient descent based approach is derived to efficiently find a solution. A large-scale simulation study is performed on four synthetic and nine cancer microarray dataseis that demonstrates the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsJieping Ye, Suresh Venkatasubramanian
PublisherSociety for Industrial and Applied Mathematics Publications
Pages73-81
Number of pages9
ISBN (Electronic)9781510811522
StatePublished - Jan 1 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
CountryCanada
CityVancouver
Period4/30/155/2/15

Keywords

  • Bioinformatics
  • Feature selection
  • Nonlinear regression

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Sun, Y., Yao, J., & Goodison, S. (2015). Feature selection for nonlinear regression and its application to cancer research. In J. Ye, & S. Venkatasubramanian (Eds.), SIAM International Conference on Data Mining 2015, SDM 2015 (pp. 73-81). Society for Industrial and Applied Mathematics Publications.