Lexidate: Model Evaluation and Selection with Lexicase

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{hernandez:2024:GECCOcomp,

• author = "Jose Guadalupe Hernandez and Anil Kumar Saini and Jason H Moore",
• title = "Lexidate: Model Evaluation and Selection with Lexicase",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Jean-Baptiste Mouret and Kai Qin",
• pages = "279--282",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, lexicase selection, AutoML, cross-validation, model evaluation, model selection, model complexity, Evolutionary Machine Learning: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654265",
• size = "4 pages",
• abstract = "Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages the accuracy of a pipeline's predictions. This single value, however, may not fully describe the generalizability of the pipeline. Here, we present Lexicase-based Validation (lexidate), a method that uses multiple, independent prediction values for selection. Lexidate splits training data into a learning set and a selection set. Pipelines are trained on the learning set and make predictions on the selection set. The predictions are graded for correctness and used by lexicase selection to identify parent pipelines. Compared to 10-fold CV, lexicase reduces the training time. We test the effectiveness of three lexi-date configurations within the Tree-based Pipeline Optimization Tool 2 (TPOT2) package on six OpenML classification tasks. In one configuration, we detected no difference in the accuracy of the final model returned from TPOT2 on most tasks compared to 10-fold CV. All configurations studied here returned similar or less complex final pipelines compared to 10-fold CV.",
• notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jose Guadalupe Hernandez Anil Kumar Saini Jason H Moore

Citations