An improved naive Bayes classifier-based noise detection technique for classifying user phone call behavior

Iqbal H. Sarker, Ashad Kabir, Alan Colman, Jun Han

Research output: Book chapter/Published conference paperConference paperpeer-review

5 Citations (Scopus)


The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The classification accuracy may decrease and the complexity of the classifiers may increase due to the number of redundant training samples. To detect such noisy instances from a training dataset, researchers use naive Bayes classifier (NBC) as it identifies misclassified instances by taking into account independence assumption and conditional probabilities of the attributes. However, some of these misclassified instances might indicate usages behavioral patterns of individual mobile phone users. Existing naive Bayes classifier based noise detection techniques have not considered this issue and, thus, are lacking in classification accuracy. In this paper, we propose an improved noise detection technique based on naive Bayes classifier for effectively classifying users’ phone call behaviors. In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals. We dynamically determine a noise threshold according to individual’s unique behavioral patterns by using both the naive Bayes classifier and Laplace estimator. We use this noise threshold to identify noisy instances. To measure the effectiveness of our technique in classifying user phone call behavior, we employ the most popular classification algorithm (e.g., decision tree). Experimental results on the real phone call log dataset show that our proposed technique more accurately identifies the noisy instances from the training datasets that leads to better classification accuracy.
Original languageEnglish
Title of host publicationData mining
Subtitle of host publication15th Australasian conference, AusDM 2017, revised selected papers
EditorsYee Ling Boo, David Stirling, Lianhua Chi, Lin Liu, Kok-Leong Ong, Graham Williams
Place of PublicationSingapore
Number of pages14
ISBN (Electronic)9789811302923
ISBN (Print)9789811302916
Publication statusPublished - 14 Apr 2018
EventThe 15th Australasian Data Mining Conference: AusDM 2017 - Crown Metropol, Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017 (Conference website)

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceThe 15th Australasian Data Mining Conference
OtherThe Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM’02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Since 2006, all proceedings have been printed as volumes in the CRPIT series. Built on this tradition, AusDM’17 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. Specifically, the conference seeks to showcase: Research Prototypes; Industry Case Studies; Practical Analytics Technology; and Research Student Projects. AusDM’16 will be a meeting place for pushing forward the frontiers of data mining in academia and industry. This year, AusDM’17 is proud to be co-located with numerous conferences including IJCAI, AAI, KSEM and IFIP in Melbourne, Australia.
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