Considerations of the nature of the relationship between generalization and interpretability in evolutionary fuzzy systems

Authors:
Adam Mostafa Ghandar;Zbigniew Michalewicz
Affiliations:
University of Adelaide, Adelaide, Australia;Unversity of Adelaide, Polish Academy of Sciences, Polish-Japanese Institute of Information Technology, Adelaide, Australia
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

Citing 4
Cited 0

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

International Journal of Approximate Reasoning
MOCell: A cellular genetic algorithm for multiobjective optimization

International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Performance out of sample is a clear determinant of the usefulness of any prediction model regardless of the application. Fuzzy knowledge base systems are also useful due to interpretability; this factor is often cited as an advantage over black box systems which make model verification by expert users more difficult. Here we examine additional advantages of interpretability for promoting general performance out side training data.