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	Neural network fusion strategies for identifying breast masses
	Wu, Y. F. ( Schools of Information Engineering Beijing University of Posts and Telecommunications) ; He, J. J. ( School of Computer Science and Technology Beijing University of Posts and Telecommunications) ; Man, Y. ( School of Computer Science and Technology Beijing University of Posts and Telecommunications) ; Arribas, J. I. ( Department of Electrical Engineering ETSl Telecomunicacion, Universidad de Valladolid)
	2013-12-12
英文摘要	In this work, we introduce the perceptron average neural network fusion strategy and implemented a number of other fusion strategies to identify breast masses in mammograms as malignant or benign with both balanced and imbalanced input features. We numerically compare various fixed and trained fusion rules, i.e., the majority vote, simple average, weighted average, and perceptron average, when applying them to a binary statistical pattern recognition problem. To judge from the experimental results, the weighted average approach outperforms the other fusion strategies with balanced input features, while the perceptron average is superior and achieves the goals with lowest standard deviation with imbalanced ensembles. We concretely analyze the results of above fusion strategies, state the advantages of fusing the component networks, and provide our particular broad sense perspective about information fusion in neural networks.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70731]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Wu, Y. F. ,He, J. J. ,Man, Y. ,et al. Neural network fusion strategies for identifying breast masses[J],2013.
APA	Wu, Y. F. ,He, J. J. ,Man, Y. ,&Arribas, J. I. .(2013).Neural network fusion strategies for identifying breast masses..
MLA	Wu, Y. F. ,et al."Neural network fusion strategies for identifying breast masses".(2013).

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