Feature subset selection for cancer detection using various rank-based algorithms Online publication date: Tue, 06-Jul-2021
by B. Surendiran; P. Sreekanth; E. Sri Hari Keerthi; M. Praneetha; D. Swetha; N. Arulmurugaselvi
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 13, No. 4, 2021
Abstract: Feature selection in data mining is the process of identifying the profitable features that are more significant in giving accurate results. Feature selection approaches like filter method and wrapper method are used here to get the more significant attributes. These methods generate the list of highly important attributes by using various ranker algorithms like correlation, relief-F, information gain, Gini index and classifiers like OneR, support vector machine, naive Bayes, random tree. In this, we are using ranker methods to perform feature selection on breast cancer analysis. Various experiments have been carried out on breast cancer Coimbra dataset using different classifiers to predict the accuracy. The crucial attributes are identified using feature selection methods, analysed for both balanced and unbalanced datasets and classified using OneR classifier.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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:
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
Our Blog 
Follow us on Twitter 
Visit us on Facebook