Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6862685 | Knowledge-Based Systems | 2013 | 10 Pages |
Abstract
The aim of this paper is to analyze the effectiveness of 36 training set selection methods when combined with genetic fuzzy rule-based classification systems. Using 37 datasets of different sizes we show that some of these methods can considerably help to reduce the computational time of the evolutionary process and to decrease the complexity of the fuzzy rule-based models with a very limited decrease of their accuracy with respect to the models generated by using the overall training set.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Michela Fazzolari, Bruno Giglio, Rafael Alcalá, Francesco Marcelloni, Francisco Herrera,