Minimal Learning Machine for Interval-Valued Data
| Autor: | Alisson S. C. Alencar, Nykolas Mayko Maia Barbosa, João P. P. Gomes, Diego Farias de Oliveira, Leonardo Ramos Rodrigues |
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| Rok vydání: | 2018 |
| Předmět: |
Hyperparameter
0209 industrial biotechnology Computer science business.industry 02 engineering and technology Machine learning computer.software_genre Data modeling Task (project management) Euclidean distance 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Task analysis 020201 artificial intelligence & image processing Artificial intelligence Minification business Nonlinear regression computer |
| Zdroj: | BRACIS |
| DOI: | 10.1109/bracis.2018.00040 |
| Popis: | Solving regression problems with interval-valued datasets is a challenging task that may arise in many real world applications. Motivated by that fact, many researchers have proposed nonlinear regression methods to handle interval-valued data in recent years. In this paper, we propose two variants of the Minimal Learning Machine (MLM) for interval-valued data. The choice of MLM is explained by its remarkable performance in many applications and the need of a single hyperparameter definition. We present a performance comparison between our methods and five benchmark nonlinear regression methods. The proposed methods presented competitive results. |
| Databáze: | OpenAIRE |
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