Output details
11 - Computer Science and Informatics
University of Kent
Output title
Improving the interpretability of classification rules discovered by an ant colony algorithm
Type
E - Conference contribution
Name of conference/published proceedings
GECCO '13 Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation
Volume number
15
Issue number
-
First page of article
73
ISSN of proceedings
-
Year of publication
2013
URL
-
Number of additional authors
1
Additional information
<24> This paper proposes a new method for the discovery of unordered classification rules to improve the interpretability of the rules discovered by an Ant Colony Optimization (ACO) classification algorithm, an important problem often ignored in the literature. The paper also proposes a new measure of rule interpretability, which can be used to analyse rules discovered by any type of rule induction algorithm, not just ACO. This paper received the best in track award (out of 20 papers in the ACO and Swarm Intelligence track) of the ACM GECCO 2013 conference, the top conference in the field of evolutionary computation.
Interdisciplinary
-
Cross-referral requested
-
Research group
I - Computational Intelligence Group
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
