Logistic regression using covariates obtained by product-unit neural network models

Authors:
César Hervás-Martínez;Francisco Martínez-Estudillo
Affiliations:
Department of Computing and Numerical Analysis, University of Córdoba, Spain;Department of Management and Quantitative Methods, ETEA, Spain
Venue:
Pattern Recognition
Year:
2007

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Abstract

We propose a logistic regression method based on the hybridation of a linear model and product-unit neural network models for binary classification. In a first step we use an evolutionary algorithm to determine the basic structure of the product-unit model and afterwards we apply logistic regression in the new space of the derived features. This hybrid model has been applied to seven benchmark data sets and a new microbiological problem. The hybrid model outperforms the linear part and the nonlinear part obtaining a good compromise between them and they perform well compared to several other learning classification techniques. We obtain a binary classifier with very promising results in terms of classification accuracy and the complexity of the classifier.