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- 56 creator adam-ghandar.
- 56 creator ralf-zurbruegg.
- 56 creator zbigniew-michalewicz.
- 56 type InProceedings.
- 56 label "A Case for Learning Simpler Rule Sets with Multiob jective Evolutionary Algorithms".
- 56 sameAs 56.
- 56 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 (fuzzy) rule based systems is a distinguishing feature, and advantage, over possible alternate approaches that are perceived as a “black box”, 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 both art and science to emphasize intuition and experience aspects of the process: both are aspects of intelligence notoriously 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”.".
- 56 hasAuthorList authorList.
- 56 isPartOf proceedings.
- 56 keyword "Evolutionary Computation".
- 56 keyword "Fuzzy Systems".
- 56 keyword "Multiobjective optimization".
- 56 title "A Case for Learning Simpler Rule Sets with Multiob jective Evolutionary Algorithms".