Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix

Table of Links

Abstract and Introduction

Related work

The proposed pipeline

Evaluation

Conclusion & Acknowledgements

References & Appendix

References

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Appendix A. Hyperparameters used during training.

This appendix contains the values of the hyperparameters finally chosen as the best for each method and dataset, listed in Tables A.9 and A.10. DAEF [26], OS-ELM [38], and OC-SVM [39] respectively.

• Deep Autoencoder for Federated learning (DAEF)[26].

– Architecture: Neurons per layer.

– λhid: Regularization hyperparameter of the hidden layer.

– λlast: Regularization hyperparameter of the last layer.

– µ: Anomaly threshold.

• Online Sequential Extreme Learning Machine (OS-ELM)[38]

– Architecture: Neurons per layer.

– µ: Anomaly threshold.

• One-Class Support Vector Machine (OC-SVM)[39].

– An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors (ν).

– Kernel type: Linear, Polynomial or RBF.

– Kernel coefficient γ (in the case of polynomial and RBF kernels).

– Degree (in the case of polynomial kernel).