Adaptive improved binary PSO-based learnable Bayesian classifier for dimensionality reduced microarray data
by Sahu Barnali; Dehuri Satchidananda; Jagadev Alok Kumar
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 11, No. 3, 2019

Abstract: This article presents, an adaptive improved binary particle swarm optimisation-based learnable Bayesian classifier for dimensionally reduced microarray data. In the first fold of this two-folded work, the problem of dimension has been reduced by unsupervised method of feature reduction. The k-means clustering algorithm has been applied on the microarray data to group functionally redundant genes followed by application of signal-to-noise-ratio ranking technique to generate an intermediate feature subset consisting of most relevant and non-redundant feature subsets. In the second fold, the feature subset has been given to an adaptive binary particle swarm optimisation-based learnable Bayesian classifier for simultaneous selection of features and classification. We have conducted an extensive experimental work on a few benchmark datasets to validate its classification accuracy with and without reducing the dimensionality of microarray data. It was observed that our method is not only accepted as a good classifier over methods, which are considered here for comparison but also be treated as an alternative method of reducing dimension of the problem.

Online publication date: Mon, 19-Aug-2019

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 Medical Engineering and Informatics (IJMEI):
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