DocumentCode
3440927
Title
Neural Network Ensemble Based on K-Means Clustering Individual Selection and Application for Software Reliability Prediction
Author
Li Kewen ; Zhao Kang ; Liu Wenying
Author_Institution
Coll. of Comput. & Commun. Eng., China Univ. of Pet. Qingdao, Qingdao, China
fYear
2013
fDate
3-4 Dec. 2013
Firstpage
131
Lastpage
135
Abstract
A novel neural network ensemble is proposed and applied to the software reliability prediction in the paper which based on the K-means clustering individual selection. First, multiple neural networks are generated by changing the structure of the neural network, then individual selection ensemble is made with K-means clustering method, and finally the outputs of these selected individuals by entropy weight method are integrated. The new method has been proved superior in software reliability prediction by experimental comparison.
Keywords
neural nets; pattern clustering; prediction theory; software reliability; K-means clustering individual selection; entropy weight method; neural network ensemble; software reliability prediction; Accuracy; Algorithm design and analysis; Clustering algorithms; Entropy; Neural networks; Prediction algorithms; Software reliability; ensemble model; individual selection; neural network; software reliability;
fLanguage
English
Publisher
ieee
Conference_Titel
Software Engineering (WCSE), 2013 Fourth World Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4799-2882-8
Type
conf
DOI
10.1109/WCSE.2013.25
Filename
6754275
Link To Document