• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

On the Performance of Cuckoo Search and Bat Algorithms Based Instance Selection Techniques for SVM Speed Optimization with Application to e-Fraud Detection

Vol. 12, No.3, March 31, 2018
10.3837/tiis.2018.03.021, Download Paper (Free):

Abstract

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filterbased and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.


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Cite this article

[IEEE Style]
Andronicus Ayobami AKINYELU and Aderemi Oluyinka ADEWUMI, "On the Performance of Cuckoo Search and Bat Algorithms Based Instance Selection Techniques for SVM Speed Optimization with Application to e-Fraud Detection," KSII Transactions on Internet and Information Systems, vol. 12, no. 3, pp. 1348-1375, 2018. DOI: 10.3837/tiis.2018.03.021

[ACM Style]
AKINYELU, A. A. and ADEWUMI, A. O. 2018. On the Performance of Cuckoo Search and Bat Algorithms Based Instance Selection Techniques for SVM Speed Optimization with Application to e-Fraud Detection. KSII Transactions on Internet and Information Systems, 12, 3, (2018), 1348-1375. DOI: 10.3837/tiis.2018.03.021