Binary and multinomial classification through evolutionary symbolic regression

Moshe Sipper

doi:10.1145/3520304.3528922

Binary and multinomial classification through evolutionary symbolic regression

Moshe Sipper

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

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.

Original language	English
Title of host publication	GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Place of Publication	New York, NY, USA
Publisher	Association for Computing Machinery, Inc
Pages	300-303
Number of pages	4
ISBN (Electronic)	9781450392686
DOIs	https://doi.org/10.1145/3520304.3528922
State	Published - 19 Jul 2022
Event	2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, United States Duration: 9 Jul 2022 → 13 Jul 2022

Conference

Conference	2022 Genetic and Evolutionary Computation Conference, GECCO 2022
Country/Territory	United States
City	Virtual, Online
Period	9/07/22 → 13/07/22

Keywords

classification
genetic programming
symbolic regression

ASJC Scopus subject areas

Artificial Intelligence
Software
Computational Mathematics
Theoretical Computer Science

Access to Document

10.1145/3520304.3528922

https://dblp.org/db/conf/gecco/gecco2022c.html#Sipper22

Cite this

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title = "Binary and multinomial classification through evolutionary symbolic regression",

abstract = "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.",

keywords = "classification, genetic programming, symbolic regression",

author = "Moshe Sipper",

note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 2022 Genetic and Evolutionary Computation Conference, GECCO 2022 ; Conference date: 09-07-2022 Through 13-07-2022",

year = "2022",

month = jul,

day = "19",

doi = "10.1145/3520304.3528922",

language = "English",

pages = "300--303",

booktitle = "GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery, Inc",

}

Sipper, M 2022, Binary and multinomial classification through evolutionary symbolic regression. in GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, New York, NY, USA, pp. 300-303, 2022 Genetic and Evolutionary Computation Conference, GECCO 2022, Virtual, Online, United States, 9/07/22. https://doi.org/10.1145/3520304.3528922

Binary and multinomial classification through evolutionary symbolic regression. / Sipper, Moshe.
GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference. New York, NY, USA: Association for Computing Machinery, Inc, 2022. p. 300-303.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Binary and multinomial classification through evolutionary symbolic regression

AU - Sipper, Moshe

PY - 2022/7/19

Y1 - 2022/7/19

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

SP - 300

EP - 303

BT - GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

PB - Association for Computing Machinery, Inc

CY - New York, NY, USA

T2 - 2022 Genetic and Evolutionary Computation Conference, GECCO 2022

Y2 - 9 July 2022 through 13 July 2022

ER -

Binary and multinomial classification through evolutionary symbolic regression

Abstract

Conference

Keywords

ASJC Scopus subject areas

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