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Effective Anomaly Detection Pipeline for Amazon Reviews: References & Appendix
by Writings, Papers and Blogs on Text ModelsJune 28th, 2024
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This study introduces an effective pipeline for detecting anomalous Amazon reviews using MPNet embeddings. It evaluates SHAP, term frequency, and GPT-3 for explainability, revealing user preferences and computational challenges. Future research may explore broader surveys and integrating GPT-3 throughout the pipeline for enhanced performance.
Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: [email protected]);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: [email protected]);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: [email protected]).
Table of Links
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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).
This paper is available on arxiv under CC 4.0 license.
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