A Gene selection approach based on the fisher linear discriminant and the neighborhood rough set. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Sun, Lin; Zhang, Xiaoyu; Xu, Jiucheng; Wang, Wei; Liu, Ruonan
• Is Part Of: Bioengineered. Volume 9:Issue 1(2018)
• Page Start: 144
• Page End: 151
• Publication Date: 2018-01-01
• Subjects: Gene selection -- Fisher linear discriminant -- neighborhood rough set -- reduction; Biomedical engineering -- Periodicals; Biotechnology -- Periodicals; Microbiology -- Periodicals; 660.6
• DOI: 10.1080/21655979.2017.1403678
• Languages: English
• ISSNs: 2165-5987

Abstract

ABSTRACT: In recent years, tumor classification based on gene expression profiles has drawn great attention, and related research results have been widely applied to the clinical diagnosis of major gene diseases. These studies are of tremendous importance for accurate cancer diagnosis and subtype recognition. However, the microarray data of gene expression profiles have small samples, high dimensionality, large noise and data redundancy. To further improve the classification performance of microarray data, a gene selection approach based on the Fisher linear discriminant (FLD) and the neighborhood rough set (NRS) is proposed. First, the FLD method is employed to reduce the preliminarily genetic data to obtain features with a strong classification ability, which can form a candidate gene subset. Then, neighborhood precision and neighborhood roughness are defined in a neighborhood decision system, and the calculation approaches for neighborhood dependency and the significance of an attribute are given. A reduction model of neighborhood decision systems is presented. Thus, a gene selection algorithm based on FLD and NRS is proposed. Finally, four public gene datasets are used in the simulation experiments. Experimental results under the SVM classifier demonstrate that the proposed algorithm is effective, and it can select a smaller and more well-classified gene subset, as well as obtain better classification performance.