Nominally piecewise multiple regression using a four-layer perceptron

Authors:
Yusuke Tanahashi;Daisuke Kitakoshi;Ryohei Nakano
Affiliations:
Nagoya Institute of Technology, Nagoya, Japan;Nagoya Institute of Technology, Nagoya, Japan;Nagoya Institute of Technology, Nagoya, Japan
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

We present a method of nominally piecewise multiple regression using a four-layer perceptron to fit multivariate data containing numerical and nominal variables. In our method, each linear regression function is accompanied with the corresponding nominal condition stating a subspace where the function is applied. Our method selects the optimal numbers of hidden units and rules very fast based on the Bayesian Information Criterion (BIC). The proposed method worked well in our experiments using an artificial and two real data sets.