A hybrid approach to improve the quality of software fault prediction using Naïve Bayes and k-NN classification algorithm with ensemble method Online publication date: Mon, 01-Oct-2018
by R. Sathyaraj; S. Prabu
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 17, No. 4, 2018
Abstract: This paper considers an improvisation in software fault prediction research area using supervised classification algorithms and it mainly focuses to increase the performance of fault prediction. In this paper, we propose a hybrid prediction model using Naïve Bayes and k-nearest neighbour classification algorithm with vote ensemble method; in short it called as hNK. The goal of this model is to predict the best classification algorithm for software fault prediction based on the metrics and attributes of datasets. In the work, we have applied training sets and testing sets in hNK model with ensemble vote and we proposed the model to identify a suitable classification algorithm for fault prediction based on the accuracy and precision. We have achieved better results using hNK model for classifying supervised algorithms with different dataset.
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 Intelligent Systems Technologies and Applications (IJISTA):
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