TY - GEN
T1 - GEML
T2 - 7th International Joint Conference on Computational Intelligence, IJCCI 2015
AU - Fitzgerald, Jeannie M.
AU - Azad, R. Muhammad Atif
AU - Ryan, Conor
N1 - Publisher Copyright:
Copyright © 2015 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2015
Y1 - 2015
N2 - This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With minor adaptations to the objective function the system can be trivially modified to work with the conceptually different paradigms of supervised, semi-supervised and unsupervised learning. The framework generates human readable solutions which explain the mechanics behind the classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. GEML is studied on a range of multi-class classification problems and is shown to be competitive with several state of the art multi-class classification algorithms.
AB - This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With minor adaptations to the objective function the system can be trivially modified to work with the conceptually different paradigms of supervised, semi-supervised and unsupervised learning. The framework generates human readable solutions which explain the mechanics behind the classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. GEML is studied on a range of multi-class classification problems and is shown to be competitive with several state of the art multi-class classification algorithms.
KW - Evolutionary Computation
KW - Grammatical Evolution
KW - Machine Learning
KW - Multi-class Classification
KW - Semi-supervised Learning
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=84961177351&partnerID=8YFLogxK
U2 - 10.5220/0005599000830094
DO - 10.5220/0005599000830094
M3 - Conference contribution
AN - SCOPUS:84961177351
T3 - IJCCI 2015 - Proceedings of the 7th International Joint Conference on Computational Intelligence
SP - 83
EP - 94
BT - ECTA
A2 - Rosa, Agostinho
A2 - Merelo, Juan Julian
A2 - Dourado, Antonio
A2 - Cadenas, Jose M.
A2 - Madani, Kurosh
A2 - Ruano, Antonio
A2 - Filipe, Joaquim
A2 - Filipe, Joaquim
PB - SciTePress
Y2 - 12 November 2015 through 14 November 2015
ER -