On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights
Author:
Affiliation:
1. Heinz College Information Systems & Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Publisher
Mary Ann Liebert Inc
Subject
Information Systems and Management,Computer Science Applications,Information Systems
Link
https://www.liebertpub.com/doi/pdf/10.1089/big.2021.0069
Reference45 articles.
1. 1. Aggarwal CC. Outlier analysis. In: Data mining. Switzerland: Springer, 2015. pp. 237–263.
2. 2. Hawkins DM. Identification of outliers, vol. 11. the Netherlands: Springer. 1980.
3. 3. Emmott AF, Das S, Dietterich T, et al. Systematic construction of anomaly detection benchmarks from real data. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description. 2013. pp. 16–21.
4. 4. Emmott A, Das S, Dietterich T, et al. A meta-analysis of the anomaly detection problem. arXiv. 2015:1503.01158.
5. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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