Abstract
When a linear regression model is constructed by statistical calculation, all the data are treated without order, even if they are order data. We propose the Geometric regression and geometric relation method (GR2) to utilize the relation information inside the order of data. The GR2 transforms the order data of each variable to a curve (or geometric relation), and uses the curves to establish a geometric regression model. The prediction method using this geometric regression model is developed to give predictions. Experimental results on simulated and real datasets show that the GR2 method is effective and has lower prediction errors than traditional linear regression.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, K., Yang, L. (2012). Geometric Linear Regression and Geometric Relation. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_31
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DOI: https://doi.org/10.1007/978-3-642-31588-6_31
Publisher Name: Springer, Berlin, Heidelberg
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