Citations & Achievements


Citing PyOD

PyOD paper is published in JMLR (machine learning open-source software track). If you use PyOD in a scientific publication, we would appreciate citations to the following paper:

@article{zhao2019pyod,
  author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
  title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
  journal = {Journal of Machine Learning Research},
  year    = {2019},
  volume  = {20},
  number  = {96},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v20/19-011.html}
}

or:

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.

Scientific Work Using or Referencing PyOD

We are appreciated that PyOD has been increasingly referred and cited in scientific works. An incomplete list is provided below:

2019

Amorim, M., Bortoloti, F.D., Ciarelli, P.M., Salles, E.O. and Cavalieri, D.C., 2019. Novelty Detection in Social Media by Fusing Text and Image Into a Single Structure. IEEE Access, 7, pp.132786-132802.

Barbariol, T., Feltresi, E. and Susto, G.A., 2019. Machine Learning approaches for Anomaly Detection in Multiphase Flow Meters. IFAC-PapersOnLine, 52(11), pp.212-217.

Fujita, H., Matsukawa, T. and Suzuki, E., 2019. Detecting outliers with one-class selective transfer machine. Knowledge and Information Systems, pp.1-38.

Gopalan, P., Sharan, V. and Wieder, U., 2019. PIDForest: Anomaly Detection via Partial Identification. In Advances in Neural Information Processing Systems (pp. 15783-15793).

Ishii, Y. and Takanashi, M., 2019. Low-cost Unsupervised Outlier Detection by Autoencoders with Robust Estimation. Journal of Information Processing, 27, pp.335-339.

Klaeger, T., Schult, A. and Oehm, L., 2019. Using anomaly detection to support classification of fast running (packaging) processes. arXiv preprint arXiv:1906.02473.

Krishnan, S. and Wu, E., 2019. AlphaClean: Automatic Generation of Data Cleaning Pipelines. arXiv preprint arXiv:1904.11827.

Kumar Das, S., Kumar Mishra, A. and Roy, P., 2019. Automatic Diabetes Prediction Using Tree Based Ensemble Learners. International Journal of Computational Intelligence & IoT, 2(2).

Li, Y., Zha, D., Zou, N. and Hu, X., 2019. PyODDS: An End-to-End Outlier Detection System. arXiv preprint arXiv:1910.02575.

Li, D., Chen, D., Jin, B., Shi, L., Goh, J. and Ng, S.K., 2019, September. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In International Conference on Artificial Neural Networks (pp. 703-716). Springer, Cham.

Meneghetti, L., Susto, G.A. and Del Favero, S., 2019. Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms. Journal of diabetes science and technology, p.1932296819881452.

Ramakrishnan, J., Shaabani, E., Li, C. and Sustik, M.A., 2019. Anomaly Detection for an E-commerce Pricing System. arXiv preprint arXiv:1902.09566.

Trinh, H.D., Giupponi, L. and Dini, P., 2019. Urban Anomaly Detection by processing Mobile Traffic Traces with LSTM Neural Networks. IEEE International Conference on Sensing, Communication and Networking (IEEE SECON).

Wan, C., Li, Z. and Zhao, Y., 2019. SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula. arXiv preprint arXiv:1904.07998.

Wang, H., Bah, M.J. and Hammad, M., 2019. Progress in Outlier Detection Techniques: A Survey. IEEE Access, 7, pp.107964-108000.

Wang, X., Du, Y., Lin, S., Cui, P., Shen, Y. and Yang, Y., 2019. adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection. Knowledge-Based Systems.

Weng, Y., Zhang, N. and Xia, C., 2019. Multi-Agent-Based Unsupervised Detection of Energy Consumption Anomalies on Smart Campus. IEEE Access, 7, pp.2169-2178.

Zhao, Y., Hryniewicki, M.K., Nasrullah, Z., and Li, Z., 2019. LSCP: Locally Selective Combination in Parallel Outlier Ensembles. SIAM International Conference on Data Mining (SDM), SIAM.

Zhao, Y., Wang, X., Cheng, C. and Ding, X., 2020. Combining Machine Learning Models and Scores using combo library. Thirty-Fourth AAAI Conference on Artificial Intelligence.

2018

Kalaycı, İ. and Ercan, T., 2018, October. Anomaly Detection in Wireless Sensor Networks Data by Using Histogram Based Outlier Score Method. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-6). IEEE.

Zhao, Y. and Hryniewicki, M.K., 2018. DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Workshop on Outlier Detection De-constructed, 2018, London, UK.

Zhao, Y. and Hryniewicki, M.K., 2018, July. XGBOD: improving supervised outlier detection with unsupervised representation learning. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.