Output details
11 - Computer Science and Informatics
University of Kent
Article title
Inducing decision trees with an ant colony optimization algorithm
Type
D - Journal article
Title of journal
Applied Soft Computing
Article number
-
Volume number
12
Issue number
11
First page of article
3615
ISSN of journal
15684946
Year of publication
2012
URL
-
Number of additional authors
2
Additional information
<24> This paper proposes a second ant colony optimization (ACO) classification algorithm for decision tree induction and is third by this group in this area. Decision trees are widely used as a comprehensible representation model, given that they can be represented in a graphical form as well as a set of classification rules. The paper compares the proposed algorithm against well-know decision tree induction algorithms – namely C4.5, CART, and the ACO-based cACDT – in an extensive empirical evaluation. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of the others.
Interdisciplinary
-
Cross-referral requested
-
Research group
I - Computational Intelligence Group
Citation count
3
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
