Weighting and Pruning of Decision Rules by Attributes and Attribute Rankings
Urszula Stańczyk · Communications in computer and information science · 2016
Pruning is a popular post-processing mechanism used in search for optimal solutions when there is insufficient domain knowledge to either limit learning data or govern induction in order to infer only the most interesting or important decision rules. Filtering of generated rules can be driven by various parameters, for example explicit rule characteristics. The paper presents research on pruning rule sets by two approaches involving attribute rankings, the first relaying on selection of rules referring to the highest ranking attributes, which is compared to weighting of rules by calculated quality measures dependent on weights coming from attribute rankings that results in rule ranking.