Gene Selection using a Hybrid Memetic and Nearest Shrunken Centroid Algorithm

Vinh Quoc Dang, Chiou-Peng Lam

Abstract

High-throughput technologies such as microarrays and mass spectrometry produced high dimensional biological datasets both in abundance and with increasing complexity. Prediction Analysis for Microarrays (PAM) is a well-known implementation of the Nearest Shrunken Centroid (NSC) method which has been widely used for classification of biological data. In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Memetic Algorithm (MA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy. Evaluation of the approach involved nine biological datasets and results showed improved feature selection stability over existing evolutionary approaches as well as improved classification accuracy.

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Paper Citation


in Harvard Style

Dang V. and Lam C. (2016). Gene Selection using a Hybrid Memetic and Nearest Shrunken Centroid Algorithm . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 190-197. DOI: 10.5220/0005665201900197


in Bibtex Style

@conference{bioinformatics16,
author={Vinh Quoc Dang and Chiou-Peng Lam},
title={Gene Selection using a Hybrid Memetic and Nearest Shrunken Centroid Algorithm},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={190-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005665201900197},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - Gene Selection using a Hybrid Memetic and Nearest Shrunken Centroid Algorithm
SN - 978-989-758-170-0
AU - Dang V.
AU - Lam C.
PY - 2016
SP - 190
EP - 197
DO - 10.5220/0005665201900197