Abstract
This paper proposes a method for finding a set of regression rules to fit data containing nominal variables as well as numerical ones. Here a regression rule is a linear regression function accompanied with the corresponding nominal condition. A set of such rules can be learned by a four-layer perceptron. A couple of model parameters are selected based on the BIC. In our experiments using 11 real data sets, the method exhibits better performance than other methods for many data sets, and found its own significance of existence in the field of regression.
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References
Tanaka, Y.: Review of the methods of quantification. Environmental Health Perspectives 32, 113–123 (1979)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Chapman and Hall, Boca Raton (1984)
Tanahashi, Y., Nakano, R.: Bidirectional clustering of MLP weights for finding nominally conditioned polynomials. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 155–164. Springer, Heidelberg (2009)
Jordan, M.I., Jacobs, R.A.: Hierarchical mixtures of experts and EM algorithm. Neural Computation 6(2), 181–214 (1994)
Friedman, J.H.: Multivariate adaptive regression splines. The Annals of Statistics 19(1), 1–141 (1991)
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1987)
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Tanahashi, Y., Nakano, R., Saito, K. (2010). Nominally Conditioned Linear Regression. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_36
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DOI: https://doi.org/10.1007/978-3-642-15825-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15824-7
Online ISBN: 978-3-642-15825-4
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