CovSel: A new approach for ensemble selection applied to symbolic regression problems.

Publikationstyp:
Konferenzbeitrag
Metadaten:
DOI
10.1145/3205455
ISBN-13
9781450356183
Name of conference
GECCO '18: Genetic and Evolutionary Computation Conference
Online publication date
2018
Datum der Veröffentlichung
2018
Status
Published
Herausgeber
ACM
Herausgeber URL
http://dx.doi.org/10.1145/3205455
Datum der Datenerfassung
2023
Titel
Proceedings of the Genetic and Evolutionary Computation Conference

Datenquelle: Crossref

Andere Metadatenquellen:
Autoren
Editoren
  • Hernán E Aguirre
  • Keiki Takadama
Zeitschrift
GECCO
Paginierung
529 - 536
Datum der Veröffentlichung
2018
Herausgeber
ACM
Herausgeber URL
https://doi.org/10.1145/3205455
Titel
CovSel: A new approach for ensemble selection applied to symbolic regression problems.

Datenquelle: DBLP

Abstract
Ensemble methods combine the predictions of a set of models to reach a better prediction quality compared to a single model's prediction. The ensemble process consists of three steps: 1) the generation phase where the models are created, 2) the selection phase where a set of possible ensembles is composed and one is selected by a selection method, 3) the fusion phase where the individual models' predictions of the selected ensemble are combined to an ensemble's estimate. This paper proposes CovSel, a selection approach for regression problems that ranks ensembles based on the coverage of correctly estimated training points and selects the ensemble with the highest coverage to be used in the fusion phase. An ensemble covers a training point if at least one of its models produces a correct prediction for this training point. The more training points are covered this way, the higher is the ensemble's coverage. The selection of the "right'" ensemble has a large impact on the final prediction. Results for two symbolic regression problems show that CovSel improves the predictions for various state-of-the-art fusion methods for ensembles composed of indepentently evolved GP models and also beats approaches based on single GP models.
Autoren
Autoren-URL
https://dl.acm.org/authorize?N695221
DOI
10.1145/3205455
Editoren
  • Hernán E Aguirre
  • Keiki Takadama
Conference finish date
2018
Zeitschrift
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
Conference place
Kyoto, Japan
Name of conference
Genetic and Evolutionary Computation Conference 2018
Notes
<!-- ACM DL Article: CovSel: A new approach for ensemble selection applied to symbolic regression problems --> <div class="acmdlitem" id="item3205570"><img src="//dl.acm.org/images/oa.gif" width="25" height="25" border="0" alt="ACM DL Author-ize service" style="vertical-align:middle"/><a href="https://dl.acm.org/authorize?N695221" title="CovSel: A new approach for ensemble selection applied to symbolic regression problems">CovSel: A new approach for ensemble selection applied to symbolic regression problems</a><div style="margin-left:25px"><a href="http://dl.acm.org/author_page.cfm?id=99659281071" >Dominik Sobania</a>, <a href="http://dl.acm.org/author_page.cfm?id=81100197550" >Franz Rothlauf</a><br />GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, 2018</div></div> <!-- ACM DL Bibliometrics: CovSel: A new approach for ensemble selection applied to symbolic regression problems--> <div class="acmdlstat" id ="stats3205570"><iframe src="https://dl.acm.org/authorizestats?N695221" width="100%" height="30" scrolling="no" frameborder="0">frames are not supported</iframe></div>
Paginierung
529 - 536
Datum der Veröffentlichung
2018
Herausgeber
ACM
Herausgeber URL
https://dl.acm.org/authorize?N695221
Datum der Datenerfassung
2019
Conference start date
2018
Titel
CovSel: A new approach for ensemble selection applied to symbolic regression problems.

Datenquelle: Manual

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