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

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.

Enhanced Feature Subset Selection Using Niche Based Bat Algorithm

“…The proposed techniques were evaluated on 24 datasets. Filter based techniques improved the training speed on all the datasets using SVM while not compromising the accuracy, whereas the wrapper based approaches improved the accuracy on 74% of the datasets with significant improvement on the training speed of SVM [27].…”

Research on the Financial Data Fraud Detection of Chinese Listed Enterprises by Integrating Audit Opinions

“…A. A. Akinyelu and A. O. Adewumi [13] adopted an improved support vector machine (SVM) model to detect electronic fraud. To solve the problem that SVM classification speed decreases with the increase of data set size, they introduced two filter-based instance selection techniques.…”

“…In this section, four mainstream classification models for comparative experiments were used to test the performance of the LightGBM model in financial fraud detection with the same data set, namely the logistic regression model [25], SVM [13], Random Forest, KNeighbors, and Three-layer CNN [15]. The results are shown in Table 11.…”