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Authors: Salama, Khalid M. | Abdelbar, Ashraf M. | Helal, Ayah M. | Freitas, Alex A.

Article Type: Research Article

Abstract: 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 ℝ 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 ℝ 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. Show more

Keywords: Machine learning, instance-based learning, lazy classifiers, Swarm Intelligence, Ant Colony Optimization

DOI: 10.3233/IDA-160031

Citation: Intelligent Data Analysis, vol. 21, no. 4, pp. 913-944, 2017