Data Aggregation for Reducing Training Data in Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Kammerer:2019:EUROCAST,

• author = "Lukas Kammerer and Gabriel Kronberger and Michael Kommenda",
• title = "Data Aggregation for Reducing Training Data in Symbolic Regression",
• booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019",
• year = "2019",
• editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia",
• volume = "12013",
• series = "Lecture Notes in Computer Science",
• pages = "378--386",
• address = "Las Palmas de Gran Canaria, Spain",
• month = "17-22 " # feb,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Machine learning, Sampling",
• isbn13 = "978-3-030-45092-2",
• DOI = "doi:10.1007/978-3-030-45093-9_46",
• abstract = "The growing volume of data makes the use of computationally intense machine learning techniques such as symbolic regression with genetic programming more and more impractical. This work discusses methods to reduce the training data and thereby also the runtime of genetic programming. The data is aggregated in a preprocessing step before running the actual machine learning algorithm. K-means clustering and data binning is used for data aggregation and compared with random sampling as the simplest data reduction method. We analyze the achieved speed-up in training and the effects on the trained models' test accuracy for every method on four real-world data sets. The performance of genetic programming is compared with random forests and linear regression. It is shown, that k-means and random sampling lead to very small loss in test accuracy when the data is reduced down to only 30percent of the original data, while the speed-up is proportional to the size of the data set. Binning on the contrary, leads to models with very high test error.",

}

Genetic Programming entries for Lukas Kammerer Gabriel Kronberger Michael Kommenda

Citations