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 :
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