A Comparative Study of GP-based and State-of-the-art Classifiers on a Synthetic Machine Learning Benchmark

被引：0

作者：

Orzechowski, Patryk ^{[1
,6
]}

Renc, Pawel ^{[2
,7
]}

La Cava, William ^{[3
]}

Moore, Jason H. ^{[4
]}

Sitek, Arkadiusz ^{[2
]}

Was, Jaroslaw ^{[5
]}

Wagenaar, Joost ^{[1
]}

机构：

[1] Univ Penn, Philadelphia, PA 19104 USA

[2] Sano, Ctr Computat Med, Krakow, Poland

[3] Harvard Med Sch, Boston Childrens Hosp, Boston, MA 02115 USA

[4] Cedars Sinai Med Ctr, Los Angeles, CA 90048 USA

[5] AGH Univ Sci & Technol, Krakow, Poland

[6] AGH Univ Sci & Technol, Dept Automat & Robot, Al Mickiewicza 30, PL-30059 Krakow, Poland

[7] AGH Univ Sci & Technol, Dept Appl Comp Sci, Al Mickiewicza 30, PL-30059 Krakow, Poland

来源：

PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 | 2022年

基金：

美国国家卫生研究院;

关键词：

benchmarking; genetic programming; machine learning; evolutionary algorithms; classification;

D O I：

10.1145/3520304.3529056

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we compare performance of genetic programming-based symbolic classifiers on a novel synthetic machine learning benchmark called DIGEN. This framework and collection of 40 different classification problems was designed specifically to differentiate performance of leading machine learning methods.

引用

页码：276 / 279

页数：4

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