Authors:
Adam Gudys
and
Marek Sikora
Affiliation:
Institute of Computer Science and Silesian University of Technology, Poland
Keyword(s):
Decision rules, Decision rules aggregation, Knowledge discovery, Convex hull, Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Decision trees and decision rules are usually applied for the classification problems in which legibility and possibility of interpretation of the obtained data model is important as well as good classification abilities. Beside trees, rules are the most frequently used knowledge representation applied by knowledge discovery algorithms. Rules generated by traditional algorithms use conjunction of simple conditions, each dividing input space by a hyperplane parallel to one of the hyperplanes of the coordinate system. There are problems for which such an approach results in a huge set of rules that poorly models real dependencies in data, is susceptible for overtfitting and hard to understand by human. Generating decision rules containing more complicated conditions may improve quality and interpretability of a rule set. In this paper an algorithm taking a set of traditional rules and aggregating them in order to obtain a smaller set of more complex rules has been presented. As procedu
re uses convex hulls, it has been called Convex Hull-Based Iterative Aggregation Algorithm.
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