Al-Jubouri, Bassma ORCID: https://orcid.org/0000-0001-7509-1478 and Gabrys, Bogdan (2014) Multicriteria Approaches for Predictive Model Generation: A Comparative Experimental Study. In: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM). IEEE, pp. 64-71

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Abstract

This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches. The first approach is a scalarized multi objective optimisation, where the models are generated from optimising a single cost function that combines the criteria. On the other hand the second approach uses a Pareto-based multi objective optimisation to trade-off the three criteria and to generate a set of non-dominated models. This study shows that defining universal measures for the three criteria is not always feasible. Furthermore, it was shown that, the models generated from Pareto-based multi objective optimisation approach can be more accurate and more diverse than the models generated from scalarized multi objective optimisation approach.

Item Type:	Book Section
Status:	Published
DOI:	10.1109/MCDM.2014.7007189
Subjects:	Q Science > Q Science (General) > Q325 Machine learning Q Science > QA Mathematics > QA75 Electronic computers. Computer science
School/Department:	York Business School
URI:	https://ray.yorksj.ac.uk/id/eprint/15973

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