@inproceedings{52d7c0d9bf364044b9aa00da9e1bfd1e,
title = "GEML: A grammatical evolution, machine learning approach to multi-class classification",
abstract = "In this paper, we propose a hybrid approach to solving multiclass problems which combines evolutionary computation with elements of traditional machine learning. 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. We investigate the effectiveness of GEML on several supervised, semi-supervised and unsupervised multiclass problems and demonstrate its competitive performance when compared with several well known machine learning algorithms. The GEML framework evolves human readable solutions which provide an explanation of the logic behind its classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. In addition we also examine the possibility of improving the performance of the algorithm through the application of several ensemble techniques.",
keywords = "Evolutionary computation, Grammatical evolution, Machine learning, Multi-class classification",
author = "Fitzgerald, {Jeannie M.} and Azad, {R. Muhammad Atif} and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 7th International Joint Conference on Computational Intelligence, IJCCI 2015 ; Conference date: 12-11-2015 Through 14-11-2015",
year = "2017",
doi = "10.1007/978-3-319-48506-5_7",
language = "English",
isbn = "9783319485041",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "113--134",
editor = "Agostinho Rosa and Joaquim Filipe and Merelo, {Juan Julian} and Correia, {Antonio Dourado} and Kurosh Madani and Cadenas, {Jose M.} and Antonio Ruano",
booktitle = "Computational Intelligence - International Joint Conference, IJCCI 2015, Revised Selected Papers",
}