Year: 2018
Type: conference-paper
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.
Institutions: Northumbria University, Ministry of Defence
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2883935747