Analyzing business process anomalies using autoencoders

DOI10.1007/s10994-018-5702-8zbMath1475.68320arXiv1803.01092OpenAlexW3126098308MaRDI QIDQ1631839

Alexander Seeliger, Stefan Luettgen, Timo Nolle, Max Mühlhäuser

Publication date: 7 December 2018

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.01092

zbMATH Keywords

autoencoderbusiness intelligenceprocess mininganomaly detectiondeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Uses Software

• Scikit
• ImageNet
• Adam
• IsolationForest
• AlexNet

Cites Work

• Support-vector networks
• Hidden Markov Models for Speech Recognition
• Learning representations by back-propagating errors
• Supervised versus unsupervised binary-learning by feedforward neural networks
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