International Journal of Computational Intelligence Systems

Volume 13, Issue 1, 2020, Pages 576 - 590

Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt

Authors
Carlos García-Martínez*, ORCID, Sebastián VenturaORCID
Computing and Numerical Analysis Department, University of Córdoba, Córdoba, 14071, Spain
*Corresponding author. Email: cgarcia@uco.es
Corresponding Author
Carlos García-Martínez
Received 29 January 2019, Accepted 9 April 2020, Available Online 25 May 2020.
DOI
https://doi.org/10.2991/ijcis.d.200511.002How to use a DOI?
Keywords
Multi-view learning; Rule-based classification; Comprehensibility; Semi-supervised learning; Co-training; Grammar-based genetic programming
Abstract

Multi-view learning analyzes the information from several perspectives and has largely been applied on semi-supervised contexts. It has not been extensively analyzed for inducing interpretable rule-based classifiers. We present a multi-view and grammar-based genetic programming model for inducing rules for semi-supervised contexts. It evolves several populations and views, and promotes both accuracy and agreement among the views. This work details how and why common practices may not produce the expected results when inducing rule-based classifiers under this methodology.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
576 - 590
Publication Date
2020/05/25
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.200511.002How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Carlos García-Martínez
AU  - Sebastián Ventura
PY  - 2020
DA  - 2020/05/25
TI  - Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt
JO  - International Journal of Computational Intelligence Systems
SP  - 576
EP  - 590
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200511.002
DO  - https://doi.org/10.2991/ijcis.d.200511.002
ID  - García-Martínez2020
ER  -