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Ant colony optimization edge selection for support vector machine speed optimization

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

Abstract

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.

Keywords

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

List of symbols

D

Dataset

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

Distance between two data instances (instances a and b)

E

Edge

HV

Heuristic value

K

Number of k-nearest neighbors

MaxG

Maximum generation

N

Size of the entire training set

NL

Neighborhood list

NR

Neighborhood range

NRuns

Number of runs for SVM cross-validation

NSub

Size of training subset

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

Training subset

Abbreviations

ABC

Artificial bee colony

Accr.

Accuracy

ACO

Ant colony optimization

ACOISA

Ant colony optimization instance selection algorithm

AFP

Accelerated flower pollination

ALO

Antlion optimization

ANN

Artificial neural network

BA

Bat algorithm

BPSO

Binary particle swarm optimization

CSA

Clonal selection algorithm

DBC

Directed bee colony

DT

Decision tree

EA

Evolutionary algorithm

ELM

Extreme learning machine

FCNN

Fast condensed nearest neighbor

FFA

Firefly algorithm

FN

False negative

FP

False positive

FPA

Flower pollination algorithm

GOA

Grasshopper optimization algorithm

GWO

Gray wolf optimization

IG

Information gain

ISDSP

Instance selection based on dense spatial partitions

IWD

Intelligent water drop

k-NN

k-nearest neighbor

LDIS

Local density-based instance selection

LSBO

Local set border selector

LSCO

Local set-based centroid selector

LSSM

Local set-based smoother

MOCHC

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

MRMC-IWD

Master river Multiple Creeks Intelligent Water Drops

NSGA-II

Non-dominated sorting genetic algorithm

PSO

Particle swarm optimization

RBF

Radial basic function

SSA

Social spider algorithm

Stor.

Storage reduction

UCI

University of California Irvine

VQN

Vaguely quantified nearest

XLDIS

Extended local density-based instance selection

Notes

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© 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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