A case for learning simpler rule sets with multiobjective evolutionary algorithms

Authors:
Adam Ghandar;Zbigniew Michalewicz;Ralf Zurbruegg
Affiliations:
School of Computer Science, University of Adelaide, Adelaide, SA, Australia;School of Computer Science, University of Adelaide, Adelaide, SA, Australia and Institute of Computer Science, Polish Academy of Sciences and Polish-Japanese Institute of Information Technology, W ...;School of Commerce, University of Adelaide, Adelaide, SA, Australia
Venue:
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Year:
2011

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Abstract

Fuzzy rules can be understood by people because of their specification in structured natural language. In a wide range of decision support applications in business, the interpretability of rule based systems is a distinguishing feature, and advantage over, possible alternate approaches that are perceived as "black boxes", for example in facilitating accountability. The motivation of this paper is to consider the relationships between rule simplicity (the key component of interpretability) and out-of-sample performance. Forecasting has been described as both art and science to emphasize intuition and experience aspects of the process: aspects of intelligence manifestly difficult to reproduce artificially. We explore, computationally, the widely appreciated forecasting "rule-of-thumb" expressed in Ockham's principle that "simpler explanations are more likely to be correct".