Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection-Reference-Cited by-同舟云学术

Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection

Published:2023-03-01 Issue:2 Volume:20 Page:1048-1059
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Aguilar Diana Laura¹^ORCID,Medina-Pérez Miguel Angel²^ORCID,Loyola-González Octavio²^ORCID,Choo Kim-Kwang Raymond³^ORCID,Bucheli-Susarrey Edoardo¹^ORCID

Affiliation:

1. Tecnologico de Monterrey, Atizapán, Estado de México, México

2. Altair Management Consultants, Madrid, Spain

3. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

Funder

National Council of Science and Technology of Mexico

National Science Foundation

National Security Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/8858/10068314/9705133-aam.pdf

Reference56 articles.

1. Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

3. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

4. Explaining Explanations: An Overview of Interpretability of Machine Learning

5. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

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