Artificial Intelligence

    

Auto-Encoder Transposed Permutation Importance Outlier Detector

Authors: Eren Unlu

We propose an innovative, trivial yet effective unsupervised outlier detection algorithm called Auto-Encoder Transposed Permutation Importance Outlier Detector (ATPI), which is based on the fusion of two machine learning concepts, autoencoders and permutation importance. As unsupervised anomaly detection is a subjective task, where the accuracy of results can vary on the demand; we believe this kind of a novel framework has a great potential in this field.

Comments: 5 Pages.

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Submission history

[v1] 2020-10-09 20:01:48

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