Zheng, Huiru and Wang, Haiying (2012) Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations. International Journal of Machine Learning and Cybernetics, 3 (3). pp. 173-182. [Journal article]
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The ability to reveal the relevant patterns in an intuitively attractive way through incremental learning makes self-adaptive neural networks (SANNs) a power tool to support pattern discovery and visualisation. Based on the combination of the information related to both the shape and magnitude of the data, this paper introduces a SANN, which implements new similarity matching criteria and error accumulation strategies for network growth. It was tested on two datasets including a real biological gene expression dataset. The results obtained have demonstrated several significant features exhibited by the proposed SANN model for improving pattern discovery and visualisation.
|Item Type:||Journal article|
|Keywords:||Self-adaptive neural networks – Pattern discovery and visualisation – Similarity measure – Chi-squares statistics|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Mathematics
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
|Deposited By:||Dr Huiru Zheng|
|Deposited On:||31 Aug 2012 12:07|
|Last Modified:||31 Aug 2012 12:07|
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