A hybrid-ensemble based framework for microarray data gene selection
by Amirreza Rouhi; Hossein Nezamabadi-pour
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 19, No. 3, 2017

Abstract: With the advent and propagation of high-dimensional microarray data, the process of gene selection has now become far more difficult and time-consuming, and classic feature selection methods are quickly becoming obsolete. Dealing with high-dimensional biomedical data is associated with problems such as the curse of dimensionality and increased presence of redundant and irrelevant genes, which all lead to significant rise in classification error. This paper provides a framework for combined use of ensemble and hybrid methods for gene selection in high-dimensional data with the aim of increasing classification accuracy and reducing dimensionality. The proposed method is benchmarked using several microarray datasets. The comparison results with those of latest ensemble feature selection methods confirm the good performance of the proposed approach.

Online publication date: Thu, 05-Apr-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com