Embedded binary PSO integrating classical methods for multilevel improved feature selection in liver and kidney disease diagnosis
by Gunasundari Selvaraj; Janakiraman Subbiah; Meenambal Selvaraj
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 31, No. 2, 2019

Abstract: Feature selection is an important pre-processing technique in the field of data mining. This process removes irrelevant data thereby reduces the number of features. This paper presents a new algorithm called embedded binary particle swarm optimisation (BPSO) to improve the performance of BPSO for feature selection. Embedded BPSO incorporates classical methods to select elite feature subset. The population is refined or extended at regular intervals using the best features from sequential forward selection and sequential backward selection methods. In this study, probabilistic neural network and support vector machine with threefold cross-validation are used to evaluate the particles. The embedded algorithm is verified in the feature selection module of the liver and kidney cancer diagnostic system. The elite features extracted from wrapper based embedded algorithm are used to characterise diseases using the classifier. Findings show that the proposed system is proficient in selecting the best features with minimum error rate.

Online publication date: Mon, 09-Sep-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 Biomedical Engineering and Technology (IJBET):
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