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To address such problem, this study proposes a Sequence Clustering-based Automated Rule Generation (SCARG) that can automatically generate rules by mining decision-making history of domain experts based on sequence clustering and probabilistic graphical modeling.
The bounded-error approach is applied to generate rules for the model using available data.
Then, some theorems for pruning tree nodes that cannot generate rules satisfying the class constraints are provided and proved.
We use all known classes of enzymes that work on carbohydrates to generate rules for converting compounds and for generating possible paths between compounds.
In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining.
An illumination mapping between the work plane and the horizontal plane was derived for sensor placement to generate rules for sensor layout.
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The purpose of this step was also to generate rule sets with a balanced number of rules for the three conclusions.
However, the number of generated rules is generally very high and must be filtered according to user-defined criteria and usefulness measures.
Most generated rules, however, are either redundant or insignificant.
XRML allows the identification of rules from Web pages and generates rules automatically.
The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com