Authors:
Jeannie M. Fitzgerald
;
R. Mohammed Atif Azad
and
Conor Ryan
Affiliation:
University of Limerick, Ireland
Keyword(s):
Unsupervised Learning, Semi-supervised Learning, Multi-class Classification, Grammatical Evolution, Evolutionary Computation, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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.