Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks
By Ester Bernadó-Mansilla, Josep Maria Garrell i Guiu. Evolutionary Computation, 2003
360 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Genetics
View Paper: https://www.semanticscholar.org/paper/50cdbacf0d7db4399a2885bd0a4842bdef03a51b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Salvador Garcia
- Alberto Fernández
- Julián Luengo
- Jason H. Moore
- Jeff Kiralis
- Jonathan M. Fisher
- Ryan J. Urbanowicz
In Genetics
- Jason H. Moore
- Ryan J. Urbanowicz
- Jonathan M. Fisher
- Jeff Kiralis
- Alberto Fernández
- Julián Luengo
- Francisco Herrera
- Salvador Garcia
Citations in the corpus
- Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, methodology): “In [84] it is proposed an extension of UCS algorithm, a recent Michigan-style genetic learning algorithm for classification [10].”
- A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability (2009, methodology): “In some cases, the t test is accompanied with the non-para-metric Wilcoxon test applied over multiple data-sets (Bernadó-Mansilla and Garrell 2003; Tulai and Oppacher 2004).”
- Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection (2012, methodology): “A model was considered to have been successfully detected if UCS correctly specified all SNPs from the correct underlying model more frequently than all other non-predictive SNPs in the respective dataset.”