TY - GEN
T1 - Binary and multinomial classification through evolutionary symbolic regression
AU - Sipper, Moshe
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/7/9
Y1 - 2022/7/9
N2 - We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf CartesianClf and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms - -XGBoost, LightGBM, and a deep neural network - -we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.
AB - We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf CartesianClf and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms - -XGBoost, LightGBM, and a deep neural network - -we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.
KW - classification
KW - genetic programming
KW - symbolic regression
UR - https://www.scopus.com/pages/publications/85134911333
U2 - 10.1145/3520304.3528922
DO - 10.1145/3520304.3528922
M3 - Conference contribution
AN - SCOPUS:85134911333
T3 - GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
SP - 300
EP - 303
BT - GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
T2 - 2022 Genetic and Evolutionary Computation Conference Companion , GECCO 2022
Y2 - 9 July 2022 through 13 July 2022
ER -