{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:24:21Z","timestamp":1778757861463,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation","award":["LZ21F030001"],"award-info":[{"award-number":["LZ21F030001"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["62172372"],"award-info":[{"award-number":["62172372"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["2022KG0AN01"],"award-info":[{"award-number":["2022KG0AN01"]}]},{"name":"Natural Science Foundation of China","award":["LZ21F030001"],"award-info":[{"award-number":["LZ21F030001"]}]},{"name":"Natural Science Foundation of China","award":["62172372"],"award-info":[{"award-number":["62172372"]}]},{"name":"Natural Science Foundation of China","award":["2022KG0AN01"],"award-info":[{"award-number":["2022KG0AN01"]}]},{"name":"Exploratory Research Project of Zhejiang Lab","award":["LZ21F030001"],"award-info":[{"award-number":["LZ21F030001"]}]},{"name":"Exploratory Research Project of Zhejiang Lab","award":["62172372"],"award-info":[{"award-number":["62172372"]}]},{"name":"Exploratory Research Project of Zhejiang Lab","award":["2022KG0AN01"],"award-info":[{"award-number":["2022KG0AN01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, it is becoming increasingly complicated but essential to provide interpretations for outlier detection results in high-dimensional space as a consequence of the large number of features. To alleviate these issues, we propose a new model based on a Variational AutoEncoder and Genetic Algorithm (VAEGA) for detecting outliers in subspaces of high-dimensional data. The proposed model employs a neural network to create a probabilistic dimensionality reduction variational autoencoder (VAE) that applies its low-dimensional hidden space to characterize the high-dimensional inputs. Then, the hidden vector is sampled randomly from the hidden space to reconstruct the data so that it closely matches the input data. The reconstruction error is then computed to determine an outlier score, and samples exceeding the threshold are tentatively identified as outliers. In the second step, a genetic algorithm (GA) is used as a basis for examining and analyzing the abnormal subspace of the outlier set obtained by the VAE layer. After encoding the outlier dataset\u2019s subspaces, the degree of anomaly for the detected subspaces is calculated using the redefined fitness function. Finally, the abnormal subspace is calculated for the detected point by selecting the subspace with the highest degree of anomaly. The clustering of abnormal subspaces helps filter outliers that are mislabeled (false positives), and the VAE layer adjusts the network weights based on the false positives. When compared to other methods using five public datasets, the VAEGA outlier detection model results are highly interpretable and outperform or have competitive performance compared to current contemporary methods.<\/jats:p>","DOI":"10.3390\/a15110429","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:33:48Z","timestamp":1668566028000},"page":"429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Auto-Encoder with Genetic Algorithm for High Dimensional Data: Towards Accurate and Interpretable Outlier Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiamu","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Jaward","family":"Bah","sequence":"additional","affiliation":[{"name":"Big Data Intelligence Research Center, Zhejiang Lab, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youwen","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoming","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hawkins, S., He, H., Williams, G., and Baxter, R. 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