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 contributionResearch

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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

Fingerprint

Fuzzy inference
Chemical activation
Neurons
Deterioration
Genetic algorithms
Neural networks

Cite this

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
Zuo, Zheming ; Li, Jie ; Wei, Bo ; Yang, Longzhi ; Fei, Chao ; Naik, Nitin. / Adaptive Activation Function Generation Through Fuzzy Inference for Grooming Text Categorisation. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 . 2019.
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title = "Adaptive Activation Function Generation Through Fuzzy Inference for Grooming Text Categorisation",
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.",
author = "Zheming Zuo and Jie Li and Bo Wei and Longzhi Yang and Chao Fei and Nitin Naik",
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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 . 2019 IEEE International Conference on Fuzzy Systems, New Orleans, United States, 23/06/19.

Adaptive Activation Function Generation Through Fuzzy Inference for Grooming Text Categorisation. / Zuo, Zheming; Li, Jie; Wei, Bo; Yang, Longzhi; Fei, Chao; Naik, Nitin.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 . 2019.

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

TY - GEN

T1 - Adaptive Activation Function Generation Through Fuzzy Inference for Grooming Text Categorisation

AU - Zuo, Zheming

AU - Li, Jie

AU - Wei, Bo

AU - Yang, Longzhi

AU - Fei, Chao

AU - Naik, Nitin

PY - 2019/6/23

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N2 - 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.

AB - 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.

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