skip to main content
10.1145/3230905.3230910acmotherconferencesArticle/Chapter ViewAbstractPublication PageslopalConference Proceedingsconference-collections
research-article

A gene expression programming method for multi-target regression

Published: 02 May 2018 Publication History

Abstract

The study of problems that involve data examples associated with multiple targets at the same time has gained a lot of attention in the past few years. In this work, a method based on gene expression programming for the multi-target regression problem is proposed. This method solves the symbolic regression problem for multi-target contexts, allowing the construction of a model, without previous knowledge of any of its elements, that fits a set of cases. Our proposal directly handles the multi-target data, encoding the individuals with a chromosome of several genes, where each gene constructs a mathematical expression that is related to a target variable. The operators used into the evolutionary process enable the constant creation of new genetic material, and some of them may favour the detection of the existing dependencies between target variables. The experimental stage showed the benefits of the gene expression programming paradigm to solve the multi-target regression problem.

References

[1]
T. Aho, S. Dzžeroski B. Ženko, and T. Elomaa. 2009. Multi-target regression with rule ensembles. Journal of Machine Learning Research 373 (2009), 2055--2066.
[2]
L. Baldassarre, L. Rosasco, A. Barla, and A. Verri. 2012. Multi-output learning via spectral filtering. Machine Learning 87, 3 (2012), 259--301.
[3]
H. Borchani, G. Varando, C. Bielza, and P. Larrañaga. 2015. A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5, 5 (2015), 216--233.
[4]
Y. Chen, D. Chen, S. U. Khan, J. Huang, and C. Xie. 2013. Solving symbolic regression problems with uniform design-aided gene expression programming. Journal of Supercomputing 66, 1553 (2013).
[5]
C. Ferreira. 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13 (2001), 87--129.
[6]
Milton Friedman. 1940. A Comparison of Alternative Tests of Significance for the Problem of m Rankings. Annals of Mathematical Statistics 11, 1 (1940), 86--92.
[7]
Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics (1979), 65--70.
[8]
D. Kocev, S. Džeroski, M. D. White, G. R. Newell, and P. Griffioen. 2009. Using single and multi-target regression trees and ensembles to model a compound index of vegetation condition. Ecological Modelling 220, 8 (2009), 1159--1168.
[9]
J. R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.
[10]
Li.Zhao, L. Wang, and D. Cui. 2012. Hoeffding bound based evolutionary algorithm for symbolic regression. Engineering Applications of Artificial Intelligence 25, 5 (2012), 945--957.
[11]
H. S. Lopes and W. R. Weinert. 2004. Egipsys: An enhanced gene expression programming approach for symbolic regression problems. International Journal of Applied Mathematics and Computer Science 14, 3 (2004), 375--384.
[12]
G. Melki, A. Cano, V. Kecman, and S. Ventura. 2017. Multi-Target Support Vector Regression Via Correlation Regressor Chains. Information Sciences 415-416 (2017), 53--69.
[13]
J. M. Moyano, E. Gibaja, and S. Ventura. 2017. An evolutionary algorithm for optimizing the target ordering in Ensemble of Regressor Chains. In IEEE Congress on Evolutionary Computation. 2015--2021.
[14]
Y.Peng, C. Yuan, X. Qin, J. Huang, and Y. Shi. 2014. An improved Gene Expression Programming approach for symbolic regression problems. Neurocomputing 137 (2014), 293--301.
[15]
O. Reyes, A. Cano, H. Fardoun, and S. Ventura. 2018. A locally weighted learning method based on a data gravitation model for multi-target regression. International Journal of Computational Intelligence Systems 11 (2018), 282--295.
[16]
O. Reyes, C. Morell, and S. Ventura. 2014. Evolutionary feature weighting to improve the performance of multi-label lazy algorithms. Integrated Computer-Aided Engineering 21, 4 (2014), 339--354.
[17]
T. Similä and J. Tikka. 2007. Input selection and shrinkage in multiresponse linear regression. Computational Statistics & Data Analysis 52, 1 (2007), 406--422.
[18]
E. Spyromitros-Xioufis, G. Tsoumakas, W. Groves, and I. Vlahavas. 2016. Multitarget regression via input space expansion: Treating targets as inputs. Machine Learning 104, 1 (2016), 55--98.
[19]
S. Stijven, E. Vladislavleva, A. Kordon, L. Willem, and M. E. Kotanchek. 2016. Genetic Programming Theory and Practice XIII. Springer, Cham, Chapter Prime-Time: Symbolic Regression Takes Its Place in the Real World, 241--260.
[20]
A. Tsanas and A. Xifara. 2012. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings 49, 560--567 (2012).
[21]
D. Tuia, J. Verrelst, L.Alonso, F.Pérez-Cruz, and G.Camps-Valls. 2011. Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geoscience and Remote Sensing Letters 8, 4 (2011), 804--808.
[22]
Frank Wilcoxon. 1945. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1, 6 (1945), 80--83.
[23]
J. Zhong, L. Feng, and Y. S. Ong. 2017. Gene Expression Programming: A Survey. IEEE Computational Intelligence Magazine 12, 3 (2017), 54--72.
[24]
C. Zhou, W. Xiao, T. M. Tirpak, and P. C. Nelson. 2003. Evolving accurate and compact classification rules with gene expression programming. IEEE Transactions on Evolutionary Computation 7, 6 (2003), 519--531.
[25]
J. Zuo, C. J. Tang, C. Li, C. A. Yuan, and A. L. Chen. 2004. Time series prediction based on gene expression programming. In Proocedings of the International Conference on Web-Age Information Management. Berlin Heidelberg: Springer-Verlag, 55--64.

Cited By

View all
  • (2023)Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical EvolutionAlgorithms10.3390/a1608036516:8(365)Online publication date: 28-Jul-2023
  • (2023)A Kaizen Programming algorithm for multi-output regression based on a heterogeneous island modelNeural Computing and Applications10.1007/s00521-023-08335-035:13(9299-9317)Online publication date: 1-Mar-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications
May 2018
357 pages
ISBN:9781450353045
DOI:10.1145/3230905
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Gene expression programming
  2. Multi-target regression
  3. Symbolic regression

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

LOPAL '18
LOPAL '18: Theory and Applications
May 2 - 5, 2018
Rabat, Morocco

Acceptance Rates

LOPAL '18 Paper Acceptance Rate 61 of 141 submissions, 43%;
Overall Acceptance Rate 61 of 141 submissions, 43%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)1
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical EvolutionAlgorithms10.3390/a1608036516:8(365)Online publication date: 28-Jul-2023
  • (2023)A Kaizen Programming algorithm for multi-output regression based on a heterogeneous island modelNeural Computing and Applications10.1007/s00521-023-08335-035:13(9299-9317)Online publication date: 1-Mar-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media