| Title | A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data |
| Publication Type | Conference Paper |
| Year of Publication | 2009 |
| Authors | Palacios, Ana M., Sánchez Luciano, and Couso Inés |
| Editor | Corchado, Emilio, Wu Xindong, Oja Erkki, Herrero Álvaro, and Baruque Bruno |
| Conference Name | Hybrid Artificial Intelligence Systems |
| Pagination | 654–661 |
| Publisher | Springer Berlin Heidelberg |
| Conference Location | Berlin, Heidelberg |
| ISBN Number | 978-3-642-02319-4 |
| Abstract | Minimum risk classification problems use a matrix of weights for defining the cost of misclassifying an object. In this paper we extend a simple genetic fuzzy system (GFS) to this case. In addition, our method is able to learn minimum risk fuzzy rules from low quality data. We include a comprehensive description of the new algorithm and discuss some issues about its fuzzy-valued fitness function. A synthetic problem, plus two real-world datasets, are used to evaluate our proposal. |
