Skip to main content

Instance-based classification with ant colony optimization

Salama, Khalid M., Abdelbar, Ashraf M., Helal, Ayah, Freitas, Alex A. (2017) Instance-based classification with ant colony optimization. Intelligent Data Analysis, 21 (4). pp. 913-944. ISSN 1088-467X. (doi:10.3233/IDA-160031) (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) (KAR id:66669)

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


Instance-based learning (IBL) methods predict the class label of a new instance based directly on the distance between the new unlabeled instance and each labeled instance in the training set, without constructing a classification model in the training phase. In this paper, we introduce a novel class-based feature weighting technique, in the context of instance-based distance methods, using the Ant Colony Optimization meta-heuristic. We address three different approaches of instance-based classification: k-Nearest Neighbours, distance-based Nearest Neighbours, and Gaussian Kernel Estimator. We present a multi-archive adaptation of the ACO? algorithm and apply it to the optimization of the key parameter in each IBL algorithm and of the class-based feature weights. We also propose an ensemble of classifiers approach that makes use of the archived populations of the ACO? algorithm. We empirically evaluate the performance of our proposed algorithms on 36 benchmark datasets, and compare them with conventional instance-based classification algorithms, using various parameter settings, as well as with a state-of-the-art coevolutionary algorithm for instance selection and feature weighting for Nearest Neighbours classifiers.

Item Type: Article
DOI/Identification number: 10.3233/IDA-160031
Uncontrolled keywords: data mining, machine learning, instance-based learning, swarm intelligence
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 09 Apr 2018 12:26 UTC
Last Modified: 16 Feb 2021 13:54 UTC
Resource URI: (The current URI for this page, for reference purposes)
Freitas, Alex A.:
  • Depositors only (login required):