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What is Autoencoder

Handbook of Research on the Significance of Forensic Accounting Techniques in Corporate Governance
Autoencoder is a kind of artificial neural network. It is used to learn unsupervised efficient data encoding.
Published in Chapter:
Deep-Auto Encoders for Detecting Credit Card Fraud
Sudarshana Kerenalli (Gitam University, India), Mylara C. Reddy (GST Bengaluru, India), and A. Usha Ruby (GST Bengaluru, India)
DOI: 10.4018/978-1-7998-8754-6.ch017
Internet-based payment methods in recent times are rapidly growing both in developing and developed economies. Credit card-based payment systems are among the prominent cashless payment methods in all economies. Credit card frauds by cyber-criminals are increasing in spite of several precautionary measures. Thus, fraud detection in real-time is a challenging task. Several machine learning tasks have attempted to solve the problem. This chapter proposes a two-step method to detect credit card fraud by coupling the deep learning-machine learning approaches. In the first stage, the dimensionality of the data set is reduced to 50% by a deep auto-encoder. A machine learning classifier classifies the instances in the second stage. Among the machine learning algorithms, the CatBoost and Random Forest achieved better performance. Their performance aligned with the state-of-the-art approaches. The proposed method is robust against the labor-intensive feature selection and imbalanced class problems.
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Deep Learning for Sentiment Analysis: An Overview and Perspectives
A network composed of an encoder and a decoder that can learn compact representations of its input data in a self-supervised manner.
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Reevaluating Factor Models: Feature Extraction of the Factor Zoo
An autoencoder is a type of artificial neural network that is used to encode the data in lower dimensional representations.
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Deep Learning on Edge: Challenges and Trends
An unsupervised learning network and is used to encode an input with a representation with fewer dimensions.
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Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services
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