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Ant colony optimization edge selection for support vector machine speed optimization

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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.

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Abbreviations

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

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

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Authors and Affiliations

  1. Department of Computer Science and Informatics, University of the Free State, Bloemfontein, 9301, South Africa

    Andronicus A. Akinyelu

  2. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg, KwaZulu-Natal, 3201, South Africa

    Absalom E. Ezugwu

  3. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X54001, Durban, 4000, South Africa

    Aderemi O. Adewumi

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Akinyelu, A.A., Ezugwu, A.E. & Adewumi, A.O. Ant colony optimization edge selection for support vector machine speed optimization. Neural Comput & Applic 32, 11385–11417 (2020). https://doi.org/10.1007/s00521-019-04633-8

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