Abstract
Classification is a fundamental problem in data mining, which is central to various applications of information technology. The existing approaches for classification have been developed mainly based on exploring the intrinsic structure of dataset itself, less or no emphasis paid on simulating human sensation and perception. Understanding data is, however, highly relevant to how one senses and perceives the data. In this talk we initiate an approach for classification based on simulating the human visual sensation and perception principle. The core idea is to treat a data set as an image, and to mine the knowledge from the data in accordance with the way we observe and perceive the image. The algorithm, visual classification algorithm (VCA), from the proposed approach is formulated. We provide a series of simulations to demonstrate that the proposed algorithm is not only effective but also efficient. In particular, we show that VCA can very often bring a significant reduction of computation effort without loss of prediction capability, as compared with the prevalently adopted SVM approach. The simulations further show that the new approach potentially is very encouraging and useful.
This research was supported by the NSFC project under contract 10371097.
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Xu, Z., Meng, D., Jing, W. (2005). A New Approach for Classification: Visual Simulation Point of View. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_1
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DOI: https://doi.org/10.1007/11427445_1
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