A graphic method for choosing the best parameter value of margins for maximising in GA-Ensemble
by Dong-Yop Oh; J. Brian Gray
International Journal of Services and Standards (IJSS), Vol. 10, No. 3, 2015

Abstract: We examine how to choose the pth percentile of margins of a fitness function for GA-Ensemble and provide a new approach for an outlier diagnostic method based on an appropriately chosen pth percentile of margins in GA-Ensemble. This provides a solution from the optimised set of base classifiers with their weights using a genetic algorithm for a binary classification problem. A plot of margins vs. maximised percentile to select p is employed to decide which p to use through the gap among the margins of outliers and normal examples based on a synthetic data set as well as real data sets. GA-Ensemble using the best pth percentile of margin is superior to AdaBoost and GA-Ensemble using other pth percentile of margin for two real-world data sets from the UC-Irvine Machine Learning Repository.

Online publication date: Sun, 19-Jul-2015

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 Services and Standards (IJSS):
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