Ensemble Genetic Programming

被引:16
作者
Rodrigues, Nuno M. [1 ]
Batista, Joao E. [1 ]
Silva, Sara [1 ]
机构
[1] Univ Lisbon, Fac Ciencias, LASIGE, Lisbon, Portugal
来源
GENETIC PROGRAMMING, EUROGP 2020 | 2020年 / 12101卷
关键词
Genetic Programming; Ensemble learning; Binary classification; Machine Learning; CLASSIFICATION;
D O I
10.1007/978-3-030-44094-7_10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ensemble learning is a powerful paradigm that has been used in the top state-of-the-art machine learning methods like Random Forests and XGBoost. Inspired by the success of such methods, we have developed a new Genetic Programming method called Ensemble GP. The evolutionary cycle of Ensemble GP follows the same steps as other Genetic Programming systems, but with differences in the population structure, fitness evaluation and genetic operators. We have tested this method on eight binary classification problems, achieving results significantly better than standard GP, with much smaller models. Although other methods like M3GP and XGBoost were the best overall, Ensemble GP was able to achieve exceptionally good generalization results on a particularly hard problem where none of the other methods was able to succeed.
引用
收藏
页码:151 / 166
页数:16
相关论文
共 32 条
[1]   Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data [J].
Bhowan, Urvesh ;
Johnston, Mark ;
Zhang, Mengjie ;
Yao, Xin .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2013, 17 (03) :368-386
[2]   Evolving Teams of Predictors with Linear Genetic Programming [J].
Markus Brameier ;
Wolfgang Banzhaf .
Genetic Programming and Evolvable Machines, 2001, 2 (4) :381-407
[3]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[4]  
Byoung-Tak Zhang, 1997, Genetic Programming 1997 Proceedings of the Second Annual Conference, P336
[5]   Inducing oblique decision trees with evolutionary algorithms [J].
Cantú-Paz, E ;
Kamath, C .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2003, 7 (01) :54-68
[6]  
Chandra A., 2006, J. Math. Model. Algorithms, V5, P417, DOI [10.1007/s10852-005-9020-3, DOI 10.1007/S10852-005-9020-3]
[7]  
Chen TQ, 2016, Arxiv, DOI [arXiv:1603.02754, 10.48550/arXiv.1603.02754, DOI 10.48550/ARXIV.1603.02754, DOI 10.1145/2939672.2939785]
[8]   Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming [J].
Coelho, Andre L. V. ;
Fernandes, Everlandio ;
Faceli, Katti .
DECISION SUPPORT SYSTEMS, 2011, 51 (04) :794-809
[9]   A multi-level approach using genetic algorithms in an ensemble of Least Squares Support Vector Machines [J].
de Araujo Padilhaa, Carlos Alberto ;
Couto Barone, Dante Augusto ;
Doria Neto, Adriao Duarte .
KNOWLEDGE-BASED SYSTEMS, 2016, 106 :85-95
[10]  
de Oliveira DF, 2009, IEEE IJCNN, P1238