One-class näive bayesian classifier for toll fraud detection

Research output: Contribution to journalArticle

Abstract

In this paper, a one-class Naive Bayesian classifier (One-NB) for detecting toll frauds in a VoIP service is proposed. Since toll frauds occur irregularly and their patterns are too diverse to be generalized as one class, conventional binary-class classification is not effective for toll fraud detection. In addition, conventional novelty detection algorithms have struggled with optimizing their parameters to achieve a stable detection performance. In order to resolve the above limitations, the original Naive Bayesian classifier is modified to handle the novelty detection problem. In addition, a genetic algorithm (GA) is employed to increase efficiency by selecting significant variables. In order to verify the performance of One-NB, comparative experiments using five well-known novelty detectors and three binary classifiers are conducted over real call data records (CDRs) provided by a Korean VoIP service company. The experimental results show that One-NB detects toll frauds more accurately than other novelty detectors and binary classifiers when the toll frauds rates are relatively low. In addition, The performance of One-NB is found to be more stable than the benchmark methods since no parameter optimization is required for One-NB.

Original languageEnglish
Pages (from-to)1353-1357
Number of pages5
JournalIEICE Transactions on Information and Systems
VolumeE96-D
Issue number5
DOIs
Publication statusPublished - 2014 May
Externally publishedYes

Keywords

  • Genetic algorithm
  • Novelty detection
  • One-class Naive Bayesian classifier
  • Toll fraud detection

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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