Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification

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

Research output: Contribution to conferencePaperpeer-review

29 Citations (Scopus)
33 Downloads (Pure)


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.
Original languageEnglish
Number of pages8
Publication statusPublished - 8 Jul 2018
EventIEEE World Congress on Computational Intelligence 2018 - Windsor Barra Convention Centre, Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018


ConferenceIEEE World Congress on Computational Intelligence 2018
Abbreviated titleWCCI 2018
CityRio de Janeiro


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