Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification

Zheming Zuo, Jie Li, Philip Anderson, Longzhi Yang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    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-IEEE)
    PublisherIEEE
    Pages1-8
    Number of pages8
    ISBN (Electronic)978-1-5090-6020-7
    DOIs
    Publication statusPublished - 13 Jul 2018
    Event2018 IEEE International Conference on Fuzzy Systems - Rio de Janeiro, Brazil
    Duration: 8 Jul 201813 Jul 2018

    Conference

    Conference2018 IEEE International Conference on Fuzzy Systems
    Abbreviated titleFUZZ- IEEE 2018
    CountryBrazil
    CityRio de Janeiro
    Period8/07/1813/07/18

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  • Cite this

    Zuo, Z., Li, J., Anderson, P., & Yang, L. (2018). Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2018.8491591