| Title | Logistic regression using covariates obtained by product-unit neural network models |
|---|---|
| Authors | Hervás-Martínez C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ |
| External publication | No |
| Means | Pattern Recogn. |
| Scope | Article |
| Nature | Científica |
| JCR Quartile | 1 |
| SJR Quartile | 1 |
| JCR Impact | 2.019 |
| SJR Impact | 1.275 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-33749267856&doi=10.1016%2fj.patcog.2006.06.003&partnerID=40&md5=3ea48011278191c5dba0f2a5291fa27f |
| Publication date | 01/01/2007 |
| ISI | 000241837300005 |
| Scopus Id | 2-s2.0-33749267856 |
| DOI | 10.1016/j.patcog.2006.06.003 |
| 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. © 2006 Pattern Recognition Society. |
| Keywords | Classification (of information); Computational complexity; Evolutionary algorithms; Mathematical models; Neural networks; Problem solving; Benchmark data sets; Logistic regression; Product-unit neural network; Regression analysis |
| Universidad Loyola members |