Abstract
The regression is one of the fundamental problems in data mining, which is central to many applications of information technology. Various approaches have been presented for regression problem nowadays. However, many problems still exist, such as efficiency and model selection problem. This paper proposes a new approach to regression problem, visual regression problem (VRA) in order to resolve these problems. The core idea is to transfer the regression problem to classification problem based on Ancona theorem, which gives the mathematical equivalence between two problems; and then use visual classification approach, which is an efficient classification approach developed based on mimicking human sensation and perception principle, to solve the transformed classification problem and get an implicit regression function; and finally utilize some mathematical skills to obtain the explicit solution of the regression problem. We also provide a series of simulations to demonstrate that the proposed approach is not only effective but also efficient.
This research was supported by the NSFC project under contract 10371097.
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Meng, D., Xu, C., Jing, W. (2005). A New Approach for Regression: Visual Regression Approach. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_20
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DOI: https://doi.org/10.1007/11596448_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
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