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Partly adaptive elastic net and its application to microarray classification

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Abstract

This paper presents a new extension of the elastic net for simultaneous gene selection and microarray classification. By introducing the proper data-driven weights to the penalty terms, the partly adaptive elastic net is proposed, which can encourage an adaptive grouping effect and reduce the influence of the wrong initial estimation. A fast-solving algorithm is also developed in the line of pathwise coordinate descent. Experiments performed on the two cancer data sets are provided to verify the obtained results.

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Acknowledgments

The authors thank to referees for insightful comments, which led to significant improvement of the paper. This work was supported by the NSFC (60850004, 11171094), and Foundation of Henan Educational Committee (2011B120005).

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Authors and Affiliations

  1. College of Mathematics and Information Science, Henan Normal University, Xingxiang, 453007, People’s Republic of China

    Juntao Li & Zhihua Zhao

  2. The Seventh Research Division, Beihang University (BUAA), Beijing, 100191, People’s Republic of China

    Yingmin Jia

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  1. Juntao Li
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  2. Yingmin Jia
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  3. Zhihua Zhao
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Correspondence to Juntao Li.

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Li, J., Jia, Y. & Zhao, Z. Partly adaptive elastic net and its application to microarray classification. Neural Comput & Applic 22, 1193–1200 (2013). https://doi.org/10.1007/s00521-012-0885-6

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  • DOI: https://doi.org/10.1007/s00521-012-0885-6

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