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A comparative study of optimization models in genetic programming-based rule extraction problems.
- Source :
-
Soft Computing - A Fusion of Foundations, Methodologies & Applications . Feb2019, Vol. 23 Issue 4, p1179-1197. 19p. - Publication Year :
- 2019
-
Abstract
- In this manuscript, we identify and evaluate some of the most used optimization models for rule extraction using genetic programming-based algorithms. Six different models, which combine the most common fitness functions, were tested. These functions employ well-known metrics such as support, confidence, sensitivity, specificity, and accuracy. The models were then applied in the assessment of the performance of a single algorithm in several real classification problems. Results were compared using two different criteria: accuracy and sensitivity/specificity. This comparison, which was supported by statistical analysis, pointed out that the use of the product of sensitivity and specificity provides a more realistic estimation of classifier performance. It was also shown that the accuracy metric can make the classifier biased, especially in unbalanced databases. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 23
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 134564309
- Full Text :
- https://doi.org/10.1007/s00500-017-2836-8