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

Authors

Carlos García-Martínez^*, , Sebastián Ventura

Computing and Numerical Analysis Department, University of Córdoba, Córdoba, 14071, Spain

^*Corresponding author. Email: cgarcia@uco.es

DOI

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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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  - 10.2991/ijcis.d.200511.002
ID  - García-Martínez2020
ER  -