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

The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving away from black-box / opaque AI towards explain...

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Vydané v:	IEEE transactions on dependable and secure computing Ročník 20; číslo 2; s. 1048 - 1059
Hlavní autori:	Aguilar, Diana Laura, Medina-Perez, Miguel Angel, Loyola-Gonzalez, Octavio, Choo, Kim-Kwang Raymond, Bucheli-Susarrey, Edoardo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington IEEE 01.03.2023 IEEE Computer Society
Predmet:	Algorithms Anomalies Anomaly detection Artificial intelligence autoencoder Computational modeling Computer architecture Cybersecurity decision tree Decision trees Decoding Encoding Explainable artificial intelligence explainable artificial intelligence (XAI) Interpretable artificial intelligence Neural networks Outliers (statistics) Probabilistic models Support vector machines
ISSN:	1545-5971, 1941-0018
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