| 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 |
2.01900 |
| SJR Impact |
1.27500 |
| 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 |
| Universidad Loyola members |
|
Manage cookies