Adaptive Activation Function Generation Through Fuzzy Inference for Grooming Text Categorisation

Zheming Zuo, Jie Li, Bo Wei, Longzhi Yang, Chao Fei, Nitin Naik

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

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Abstract

The activation function is introduced to determine the output of neural networks by mapping the resulting values of neurons into a specific range. The activation functions often suffer from ‘gradient vanishing’, ‘non zero-centred function outputs’, ‘exploding gradients’, and ‘dead neurons’, which may lead to deterioration in the classification performance. This paper proposes an activation function generation approach using the Takagi-Sugeno-Kang inference in an effort to address such challenges. In addition, the proposed method further optimises the coefficients in the activation function using the genetic algorithm such that the activation function can adapt to different applications. This approach has been applied to a digital forensics application of online grooming detection. The evaluations confirm the superiority of the proposed activation function for online grooming detection using an imbalanced data set.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019
Publication statusPublished - 23 Jun 2019
Event2019 IEEE International Conference on Fuzzy Systems - New Orleans, United States
Duration: 23 Jun 201926 Jun 2019

Conference

Conference2019 IEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE
CountryUnited States
CityNew Orleans
Period23/06/1926/06/19

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    Zuo, Z., Li, J., Wei, B., Yang, L., Fei, C., & Naik, N. (2019). Adaptive Activation Function Generation Through Fuzzy Inference for Grooming Text Categorisation. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019