USP Data Stream Repository

PublicLY AVAILABLE real-world datasets to evaluate stream learning algorithms

Dataset listing

All the datasets in a ZIP file can be downloaded here or you can access the individual CSV files here

The labels are on the last column (e.g., -1 or 1 for binary datasets).

USP DS Listing

How to cite this repository?

@article{SouzaChallenges:2020, title={Challenges in Benchmarking Stream Learning Algorithms with Real-world Data}, author={Souza, V. M. A. and Reis, D. M. and Maletzke, A. G. and Batista, G. E. A. P. A.}, journal={Data Mining and Knowledge Discovery}, pages={1805-1858}, volume={34}, year={2020}, doi={10.1007/s10618-020-00698-5}}
Paper here

LICENCE

This repository is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

Share — copy and redistribute the material in any medium or format;
Adapt — remix, transform, and build upon the material for any purpose, including research and commercial use.

Under the following terms:

Attribution — appropriate credit must be given to the repository and original authors, a link to the license must be provided, and any modifications must be indicated.

Note: Some datasets included in this repository originate from external sources and may be subject to their own licenses and usage restrictions.

How to donate a new dataset for this repository?

If you want to donate a dataset with concept drift from a real-world streaming problem, please contact Vinicius Souza (vinicius.mourao at pucpr.br) or Gustavo Batista (g.batista at unsw.edu.au) answering the following questions:

- Where can we get the data in ARFF/CSV format?
- Is there a publication related to the data?
- Can you describe the classification problem related to the data, and why is it a streaming problem?
- Can you explain the order of the examples and the interval between observations?
- Can you describe the class labels?
- Do you know the type of drift (incremental, gradual, abrupt, reoccurring) which occur in the dataset and when they occur?

References

- Blackard JA, Dean DJ (1999) Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and electronics in agriculture 24(3):131–151- Dheeru D, Karra Taniskidou E (2017) UCI machine learning repository. URL http://archive.ics.edu/ml- Ditzler G, Polikar R (2013) Incremental learning of concept drift from streaming imbalanced data. ieee transactions on knowledge and data engineering 25(10):2283–2301- Harries M (1999) Splice-2 comparative evaluation: Electricity pricing. Tech. Rep. 1, University of New South Wales, Sydney, Australia- Ikonomovska E, Gama J, Dveroski S (2011) Learning model trees from evolving data streams. Data mining and knowledge discovery 23(1):128–168- Katakis I, Tsoumakas G, Banos E, Bassiliades N, Vlahavas I (2009) An adaptive personalized news dissemination system. Journal of Intelligent Information Systems 32(2):191–212- Losing V, Hammer B, Wersing H (2015) Interactive online learning for obstacle classification on a mobile robot. In: International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–8- Losing V, Hammer B, Wersing H (2016) Knn classifier with self adjusting memory for heterogeneous concept drift. In: IEEE International Conference on Data Mining (ICDM), IEEE, pp 291–300- Reis, D. M., Flach, P., Matwin, S., & Batista, G. (2016). Fast unsupervised online drift detection using incremental kolmogorov-smirnov test. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1545-1554.- Souza VMA, Silva DF, Gama J, Batista GEAPA (2015) Data stream classification guided by clustering on nonstationary environments and extreme verification latency. In: Proceedings of the 2015 SIAM International Conference on Data Mining, SIAM, pp 873–881- Souza, V. M. A. (2018). Asphalt pavement classification using smartphone accelerometer and complexity invariant distance. Engineering Applications of Artificial Intelligence, 74, 198-211.- Souza VMA, Reis DM, Maletzke AG, Batista GEAPA (2020) Challenges on Benchmarking Stream Classifiers and Drift Detectors with Real-world Evolving Data. Data Mining and Knowledge Discovery, 1-54- Souza, V. M., Parmezan, A. R., Chowdhury, F. A., & Mueen, A. (2021). Efficient unsupervised drift detector for fast and high-dimensional data streams. Knowledge and Information Systems, 63(6), 1497-1527.- Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: IEEE Symposium on computational Intelligence for Security and Defense Applications, IEEE, pp 1–6- Ulanova, L., Begum, N., Shokoohi-Yekta, M., & Keogh, E. (2016). Clustering in the face of fast changing streams. In Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 1-9. - Vergara A, Vembu S, Ayhan T, Ryan MA, Homer ML, Huerta R (2012) Chemical gas sensor drift compensation using classifier ensembles. Sensors and Actuators B: Chemical 166:320–329- Zhu X (2010) Stream data mining repository. URL www.cse.fau.edu/~xqzhu/stream.html- Zliobaite I (2011) Combining similarity in time and space for training set formation under concept drift. Intelligent Data Analysis 15(4):589–611

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