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Investigating the Impact of Various Classification Quality Measures in the Predictive Accuracy of ABC-Miner

Salama, Khalid M. and Freitas, Alex A. (2013) Investigating the Impact of Various Classification Quality Measures in the Predictive Accuracy of ABC-Miner. In: 2013 IEEE Congress on Evolutionary Computation. IEEE, pp. 2321-2328. ISBN 978-1-4799-0453-2. E-ISBN 978-1-4799-0454-9. (doi:10.1109/CEC.2013.6557846) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/CEC.2013.6557846

Abstract

Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimization (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naive-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim of this investigation is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 6 different classification measures on 25 benchmark datasets. We found that the hypothesis that different measures produce different results is acceptable according to the Friedman's statistical test.

Item Type: Book section
DOI/Identification number: 10.1109/CEC.2013.6557846
Uncontrolled keywords: ant colony optimization, data mining, machine learning, Bayesian network classifier
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 01 Jul 2013 17:01 UTC
Last Modified: 17 Sep 2019 09:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34480 (The current URI for this page, for reference purposes)
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