Michel Camilleri, Filippo Neri, Michalis Papoutsidakis, "An Algorithmic Approach to Parameter Selection in Machine Learning using Meta-Optimization Techniques," WSEAS Transactions on Systems, vol. 13, pp. 203-212, 2014, DOI:
Michel Camilleri, Filippo Neri, Michalis Papoutsidakis. An Algorithmic Approach to Parameter Selection in Machine Learning using Meta-Optimization Techniques.
WSEAS Transactions on Systems. 2014;13:203-212.
WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 13, 2014
An Algorithmic Approach to Parameter Selection in Machine Learning using Meta-Optimization Techniques
Authors: , ,
Abstract: The process of identifying the optimal parameters for an optimization algorithm or a machine learning one is usually costly, involves the search of a large, possibly infinite, space of candidate parameter sets, and may not guarantee optimality. Various attempts have been made to automate this process. Our work attempts to explore this research area further by analyzing the behavior of a simple genetic algorithm when used to find the optimal parameter setting for an ID3 like learner operating on a selected dataset.
Pages: 203-212 WSEAS Transactions on Systems, ISSN / E-ISSN: 1109-2777 / 2224-2678, Volume 13, 2014, Art. #18 DOI: *As the DOI is a unique identifier, it is already available in the pdf version.
**The DOI link will be activated in the first midst of January 2026.
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