- author = "Jeannie Fitzgerald and R. Muhammad Atif Azad and Conor Ryan",
- title = "{GEML}: Evolutionary Unsupervised and Semi-Supervised Learning of Multi-class Classification with Grammatical Evolution",
- booktitle = "ECTA. 7th International Conference on Evolutionary Computation Theory and Practice",
- year = "2015",
- editor = "Agostinho Rosa and Juan Julian Merelo and Antonio Dourado and Jose M. Cadenas and Kurosh Madani and Antonio Ruano and Joaquim Filipe",
- address = "Lisbon, Portugal",
- month = "12-14 " # nov,
- pages = "83--94",
- organisation = "INSTICC - Institute for Systems and Technologies of Information, Control and Communication, IFAC - International Federation of Automatic Control, IEEE SMC - IEEE Systems, Man and Cybernetics Society",
- publisher = "SCITEPRESS - Science and Technology Publications",
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Semi-supervised Learning, Multi-class Classification, Evolutionary Computation, Machine Learning",
- isbn13 = "978-9-8975-8165-6",
-
URL = "
http://www.researchgate.net/publication/283055687_GEML_Evolutionary_Unsupervised_and_Semi-Supervised_Learning_of_Multi-class_Classification_with_Grammatical_Evolution",
-
URL = "http://ieeexplore.ieee.org/document/7529309/",
- size = "12 pages",
- abstract = "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.",
- notes = "IJCCI Also known as \cite{7529309}
