Multicriteria approaches for predictive model generation: A comparative experimental study

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...

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Bibliographic Details
Published in:	2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) pp. 64 - 71
Main Authors:	Al-Jubouri, Bassma, Gabrys, Bogdan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2014
Subjects:	Accuracy Artificial neural networks Complexity theory Mathematical model Optimization Prediction algorithms Predictive models
Online Access:	Get full text
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Summary:	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.
DOI:	10.1109/MCDM.2014.7007189