Ant colony optimization edge selection for support vector machine speed optimization

  • Andronicus A. AkinyeluEmail author
  • Absalom E. Ezugwu
  • Aderemi O. Adewumi
Original Article


Support vector machine (SVM) is a widely used and reliable machine learning algorithm. It has been successfully applied to many real-world problems, with remarkable results. However, it has also been observed that SVM computational complexity increases with the increase in data size. Although many SVM speed optimization techniques have been proposed in the literature, there is still need for further improvement on the performance speed and accuracy of this algorithm. In this paper, a boundary detection algorithm for SVM speed optimization called ant colony optimization instance selection algorithm (ACOISA) is proposed. ACOISA is inspired by edge selection in ant colony optimization (ACO) algorithm, and it performs two primary functions: boundary detection and boundary instance selection. In the algorithm, ACO is used for boundary detection and k-nearest neighbor algorithm is used for boundary instance selection. Different sets of experiments are carried out to validate the efficiency of the proposed technique. All the experiments were performed on 35 datasets containing three well-known e-fraud types (credit card fraud, email spam and phishing email) and 31 other datasets available at UCI data repository. The experimental results showed that the proposed technique efficiently improved SVM training speed in 100% of the datasets used for evaluation, without significantly affecting SVM classification quality. Furthermore, the Freidman’s and Holm’s post hoc tests were conducted to statistically validate the credibility of the results. The statistical test results revealed that the proposed technique is significantly faster, compared to the standard SVM and some existing instance selection techniques, in all cases.


Machine learning Support vector machine Instance selection Speed optimization Ant colony optimization 

List of symbols



\( {\text{dist}}\left[ {a,b} \right] \)

Distance between two data instances (instances a and b)




Heuristic value


Number of k-nearest neighbors


Maximum generation


Size of the entire training set


Neighborhood list


Neighborhood range


Number of runs for SVM cross-validation


Size of training subset

\( T_{\text{s}} \)

Training subset



Artificial bee colony




Ant colony optimization


Ant colony optimization instance selection algorithm


Accelerated flower pollination


Antlion optimization


Artificial neural network


Bat algorithm


Binary particle swarm optimization


Clonal selection algorithm


Directed bee colony


Decision tree


Evolutionary algorithm


Extreme learning machine


Fast condensed nearest neighbor


Firefly algorithm


False negative


False positive


Flower pollination algorithm


Grasshopper optimization algorithm


Gray wolf optimization


Information gain


Instance selection based on dense spatial partitions


Intelligent water drop


k-nearest neighbor


Local density-based instance selection


Local set border selector


Local set-based centroid selector


Local set-based smoother


Multi-objective cross-generational elitist selection, heterogeneous recombination and cataclysmic mutation


Master river Multiple Creeks Intelligent Water Drops


Non-dominated sorting genetic algorithm


Particle swarm optimization


Radial basic function


Social spider algorithm


Storage reduction


University of California Irvine


Vaguely quantified nearest


Extended local density-based instance selection


Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and InformaticsUniversity of the Free StateBloemfonteinSouth Africa
  2. 2.School of Mathematics, Statistics, and Computer ScienceUniversity of KwaZulu-NatalPietermaritzburgSouth Africa
  3. 3.School of Mathematics, Statistics and Computer ScienceUniversity of KwaZulu-NatalDurbanSouth Africa

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