Title

Gene selection using a hybrid memetic and nearest shrunken centroid algorithm

Document Type

Article

Publisher

SciTePress

School

School of Computer and Security Science

RAS ID

23353

Comments

Originally published as : 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 TechnologiesISBN 978-989-758-170-0, pages 190-197. DOI: 10.5220/0005665201900197. Article found here

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. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

DOI

10.5220/0005665201900197