Grooming detection using fuzzy-rough feature selection and text classification

Zheming Zuo, Jie Li, Philip Anderson, Longzhi Yang, Nitin Naik

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Online child grooming detection has recently attracted intensive research interests from both the machine learning community and digital forensics community due to its great social impact. The existing data-driven approaches usually face the challenges of lack of training data and the uncertainty of classes in terms of the classification or decision boundary. This paper proposes a grooming detection approach in an effort to address such uncertainty based on a data set derived from a publicly available profiling data set. In particular, the approach firstly applies the conventional text feature extraction approach in identifying the most significant words in the data set. This is followed by the application of a fuzzy-rough feature selection approach in reducing the high dimensions of the selected words for fast processing, which at the same time addressing the uncertainty of class boundaries. The experimental results demonstrate the efficiency and efficacy of the proposed approach in detecting child grooming.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
PublisherIEEE
ISBN (Electronic)9781509060207
DOIs
Publication statusPublished - 12 Oct 2018
Event2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2018-July
ISSN (Print)1098-7584

Conference

Conference2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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