Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix

28 Jun 2024


(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);

(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);

(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es).

Abstract and Introduction

Related work

The proposed pipeline


Conclusion & Acknowledgements

References & Appendix


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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).

Table A.9: Hyperparameters used during the 1vs.4 experimentation.

Table A.10: Hyperparameters used during the 1vs.1 experimentation.

This paper is available on arxiv under CC 4.0 license.