Skip to main content

Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers

Salama, Khalid M., Abdelbar, Ashraf M., Otero, Fernando E.B. (2015) Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers. In: 2015 IEEE Symposium Series on Computational Intelligence. 2015 IEEE Symposium Series on Computational Intelligence. . pp. 1146-1153. IEEE ISBN 978-1-4799-7560-0. (doi:10.1109/SSCI.2015.164)

PDF - Author's Accepted Manuscript
Download (188kB) Preview
[img]
Preview
Official URL
http://dx.doi.org/10.1109/SSCI.2015.164

Abstract

Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. all). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner-M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that substantially improve on the simple Accuracy quality function that was previously used in Ant-Tree-Miner-M.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/SSCI.2015.164
Uncontrolled keywords: decision trees; prediction algorithms; training; predictive models; classification algorithms; mathematical model; electronic mail
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 26 Oct 2015 01:09 UTC
Last Modified: 14 Oct 2019 20:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/51226 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
  • Depositors only (login required):

Downloads

Downloads per month over past year