KDE-OCSVM model using Kullback-Leibler divergence to detect anomalies in medical claims-Reference-Cited by-同舟云学术

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KDE-OCSVM model using Kullback-Leibler divergence to detect anomalies in medical claims

Published:2022-08 Issue: Volume:200 Page:117056
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Wang Xiao-kang,Hou Wen-hui,Zhang Hong-yu,Wang Jian-qiang,Goh Mark,Tian Zhang-peng,Shen Kai-wen

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference36 articles.

1. Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD);Alam;Pattern Recognition Letters,2020

2. Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM;Binbusayyis;Applied Intelligence,2021

3. Towards the design of an offline signature verifier based on a small number of genuine samples for training;Bouamra;Expert Systems with Applications,2018

4. Anomaly and novelty detection for robust semi-supervised learning;Cappozzo;Statistics and Computing,2020

5. One class random forests;Désir;Pattern Recognition,2013

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