Classification Algorithm With Lexicase Selection
Abstract
The hybrid fuzzy genetic based algorithm was implemented. The genetic fuzzy systems are applied for classifier learning, and although they allow creating interpretable rule bases, the process of designing a rule base can be improved with specific genetic operators, such as lexicase selection. The influence of lexicase selection on the efficiency of its work for data classification is examined in this article. “Ring”, “Phoneme” and “Satimage” datasets were used for verification. The results were analyzed by verification using Mann-Whitney U test. According to the results of basic hybrid fuzzy genetic based algorithm with lexicase selection of several runs, where in each the duration of the search for the best rule base was limited to five hundred generations, efficiency was only on number of rules, but paired with the previously developed initialization modification, the accuracy and F-score were improved on “Satimage” dataset and the number of rules decreased on all of the datasets.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Pleshkova, T., & Stanovov, V. (2023). Classification Algorithm With Lexicase Selection. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 194-200). European Publisher. https://doi.org/10.15405/epct.23021.24