Datasets:

techynilesh
/

streamarena

Exact instance/feature counts computed directly from each CSV. #Classes is the number of unique values in the label column; for regression, the Target column name is shown instead. For anomaly detection, the percentage shown is the minority-class (anomaly) rate.

The Source column gives a best-effort attribution to each dataset's original public origin, keyed to full BibTeX entries in the References section at the bottom. These are best-effort identifications based on well-known dataset naming conventions — verify against the original publication before citing precisely in academic work.

Classification

Real-world

Dataset	#Instances	#Features	#Classes	Source
Dota	11,055	30	2	OpenML [openml]
Gisette	7,000	5,000	2	NIPS 2003 FS Challenge [guyon2004]
HAR	10,299	561	2	UCI [uci] — Human Activity Recognition Using Smartphones
KDDCup99	100,000	41	2	UCI [uci] — KDD Cup 1999
MNIST	70,000	784	2	LeCun et al. [lecun1998]
Spambase	4,601	57	2	UCI [uci] — Spambase
Usenet	18,846	658	20	Katakis et al. [katakis2010]
adult	48,842	14	4	UCI [uci] — Adult / Census Income
airlines	539,383	7	2	OpenML [openml] — Airlines
electricity	45,312	8	2	Harries [harries1999] — Electricity Market
forest_cover	581,012	54	7	UCI [uci] — Covertype
insects	52,848	33	6	Souza et al. [souza2020]
nomao	34,465	118	2	UCI [uci] — Nomao
poker	829,201	10	10	UCI [uci] — Poker Hand
real_kdd99	100,655	38	12	UCI [uci] — KDD Cup 1999 (resampled)
real_pendigits	10,992	16	10	UCI [uci] — Pen-Based Recognition of Handwritten Digits
real_powersupply	29,928	2	24	Zhu [zhu2010]
real_sensor	2,219,803	5	55	Zhu [zhu2010]
real_shuttle	58,000	9	7	UCI [uci] — Statlog (Shuttle), resampled
shuttle	58,000	9	7	UCI [uci] — Statlog (Shuttle)
vehicle_sensIT	98,528	100	3	UCI [uci] — SensIT Vehicle
weather	18,159	8	2	Elwell & Polikar [elwell2011]

Synthetic

Dataset	#Instances	#Features	#Classes	Source
Madelon	2,600	500	2	Guyon et al. [guyon2004]
RBF	10,000	10,000	2	MOA [bifet2010] — RandomRBF generator
RBFm_100k	100,000	10	5	MOA [bifet2010] — RandomRBF generator
RTG_2abrupt	100,000	30	5	MOA [bifet2010] — RandomTreeGenerator
RTG_highdim_10k	10,000	450	2	MOA [bifet2010] — RandomTreeGenerator
hyperplane_high_gradual_drift	500,000	10	2	MOA/River [bifet2010][montiel2021] — Hyperplane
movingRBF	200,000	10	5	MOA [bifet2010] — Moving RandomRBF
moving_squares	200,000	2	4	MOA [bifet2010] — Moving Squares
sea_high_abrupt_drift	500,000	3	2	Street & Kim [street2001] — SEA generator
sea_high_mixed_drift	500,000	3	2	Street & Kim [street2001] — SEA generator
sine_stream_with_drift	50,000	4	2	MOA/River [bifet2010][montiel2021] — Sine
synth_agrawal	100,000	9	2	Agrawal et al. [agrawal1993]
synth_blobs_expanding	100,000	5	5	scikit-learn [pedregosa2011] — make_blobs
synth_blobs_gradual	100,000	5	5	scikit-learn [pedregosa2011] — make_blobs
synth_blobs_incremental	100,000	5	5	scikit-learn [pedregosa2011] — make_blobs
synth_blobs_merge_split	100,000	5	5	scikit-learn [pedregosa2011] — make_blobs
synth_blobs_sudden	100,000	5	5	scikit-learn [pedregosa2011] — make_blobs
synth_rbf_fast	100,000	5	5	MOA [bifet2010] — RandomRBF generator
synth_rbf_gradual	100,000	5	5	MOA [bifet2010] — RandomRBF generator
synth_rbf_random	100,000	4	4	MOA [bifet2010] — RandomRBF generator

Regression

Real-world

Dataset	#Instances	#Features	Target	Source
House8L	22,784	8	target	DELVE [delve]
MetroTraffic	48,204	7	target	UCI [uci] — Metro Interstate Traffic Volume
WGN0331_data	15,628	7	Avg$PerMWHr	NZ Electricity Market (EMI) — node pricing data
abalone	4,977	8	target	UCI [uci] — Abalone
ailerons	13,750	40	target	DELVE [delve] — Ailerons
bike	17,379	12	cnt	UCI/Kaggle [uci] — Bike Sharing Demand
brazilian_houses	10,692	12	total	OpenML [openml] — Brazilian Houses to Rent
california_housing	20,640	8	medianHouseValue	StatLib — California Housing
cps88wages	28,155	6	wage	Current Population Survey 1988
cpu_activity	8,192	21	usr	DELVE [delve] — Comp-Activ
diamonds	53,940	9	price	ggplot2/Kaggle — Diamonds
elevators	16,599	18	target	DELVE [delve] — Elevators
fifa	19,178	28	wage_eur	Kaggle — FIFA player ratings
health_insurance	22,272	12	whrswk	AER package (R) — Health Insurance
kin8nm	8,192	8	y	DELVE [delve] — Kinematics of Robot Arm
kings_county	21,613	21	price	Kaggle — King County House Sales
miami_housing	13,932	16	SALE_PRC	Kaggle — Miami Housing
naval_propulsion_plant	11,934	15	gt_compressor_decay_state_coefficient	UCI [uci] — Naval Propulsion Plants
physiochemical_protein	45,730	9	RMSD	UCI [uci] — Physicochemical Properties of Protein Tertiary Structure
pumadyn32nh	8,192	32	thetadd6	DELVE [delve] — Puma Dynamics
sarcos	48,933	27	V22	SARCOS — robot arm inverse dynamics
superconductivity	21,263	81	critical_temp	UCI [uci] — Superconductivity Data
video_transcoding_noID	68,784	19	utime	UCI [uci] — Online Video Characteristics and Transcoding Time
wave_energy	72,000	48	energy_total	UCI [uci] — Large-scale Wave Energy Farm
white_wine	4,898	11	quality	UCI [uci] — Wine Quality (white)

Synthetic

Dataset	#Instances	#Features	Target	Source
FriedmanGra	100,000	10	target	River [montiel2021] — FriedmanDrift (global recurring abrupt)
FriedmanGsg	99,971	10	target	River [montiel2021] — FriedmanDrift (global slow gradual)
FriedmanLea	100,000	10	target	River [montiel2021] — FriedmanDrift (local expanding abrupt)
fried	40,768	10	target	Friedman [friedman1991]
hyperA	500,000	10	target	MOA/River [bifet2010][montiel2021] — Hyperplane (regression)

Clustering

Real-world

Dataset	#Instances	#Features	#Classes	Source
adult	48,842	14	4	UCI [uci] — Adult / Census Income
electricity	45,312	8	2	Harries [harries1999] — Electricity Market
forest_cover	581,012	54	7	UCI [uci] — Covertype
insects	52,848	33	6	Souza et al. [souza2020]
new_airlines	539,383	7	2	OpenML [openml] — Airlines
vehicle_sensIT	98,528	100	3	UCI [uci] — SensIT Vehicle

Synthetic

Dataset	#Instances	#Features	#Classes	Source
hyperplane_high_gradual_drift	500,000	10	2	MOA/River [bifet2010][montiel2021] — Hyperplane
movingRBF	200,000	10	5	MOA [bifet2010] — Moving RandomRBF
moving_squares	200,000	2	4	MOA [bifet2010] — Moving Squares
sea_high_abrupt_drift	500,000	3	2	Street & Kim [street2001] — SEA generator
sea_high_mixed_drift	500,000	3	2	Street & Kim [street2001] — SEA generator
synth_RandomRBFDrift	100,000	4	4	MOA [bifet2010] — RandomRBF generator
synthetic_blobs_100k_samples_5features_8clusters	100,000	5	8	scikit-learn [pedregosa2011] — make_blobs

Anomaly Detection

All 51 datasets are drawn from the ODDS Library [rayana2016] and/or the ADBench [han2022] outlier-detection benchmark suite (both aggregate datasets originally published individually, e.g. via UCI [uci]).

Dataset	#Instances	#Features	Anomaly Rate	Source
10_cover	286,048	10	0.96%	ODDS [rayana2016] / ADBench [han2022]
11_donors	619,326	10	5.93%	ODDS [rayana2016] / ADBench [han2022]
12_fault	1,941	27	34.67%	ODDS [rayana2016] / ADBench [han2022]
13_fraud	284,807	29	0.17%	ODDS [rayana2016] / ADBench [han2022]
14_glass	214	7	4.21%	ODDS [rayana2016] / ADBench [han2022]
15_Hepatitis	80	19	16.25%	ODDS [rayana2016] / ADBench [han2022]
16_http	567,498	3	0.39%	ODDS [rayana2016] / ADBench [han2022]
17_InternetAds	1,966	1,555	18.72%	ODDS [rayana2016] / ADBench [han2022]
18_Ionosphere	351	32	35.9%	ODDS [rayana2016] / ADBench [han2022]
19_landsat	6,435	36	20.71%	ODDS [rayana2016] / ADBench [han2022]
1_ALOI	49,534	27	3.04%	ODDS [rayana2016] / ADBench [han2022]
20_letter	1,600	32	6.25%	ODDS [rayana2016] / ADBench [han2022]
21_Lymphography	148	18	4.05%	ODDS [rayana2016] / ADBench [han2022]
22_magic.gamma	19,020	10	35.16%	ODDS [rayana2016] / ADBench [han2022]
23_mammography	11,183	6	2.32%	ODDS [rayana2016] / ADBench [han2022]
24_mnist	7,603	100	9.21%	ODDS [rayana2016] / ADBench [han2022]
25_musk	3,062	166	3.17%	ODDS [rayana2016] / ADBench [han2022]
26_optdigits	5,216	64	2.88%	ODDS [rayana2016] / ADBench [han2022]
27_PageBlocks	5,393	10	9.46%	ODDS [rayana2016] / ADBench [han2022]
28_pendigits	6,870	16	2.27%	ODDS [rayana2016] / ADBench [han2022]
29_Pima	768	8	34.9%	ODDS [rayana2016] / ADBench [han2022]
2_annthyroid	7,200	6	7.42%	ODDS [rayana2016] / ADBench [han2022]
30_satellite	6,435	36	31.64%	ODDS [rayana2016] / ADBench [han2022]
31_satimage-2	5,803	36	1.22%	ODDS [rayana2016] / ADBench [han2022]
32_shuttle	49,097	9	7.15%	ODDS [rayana2016] / ADBench [han2022]
33_skin	245,057	3	20.75%	ODDS [rayana2016] / ADBench [han2022]
34_smtp	95,156	3	0.03%	ODDS [rayana2016] / ADBench [han2022]
35_SpamBase	4,207	57	39.91%	ODDS [rayana2016] / ADBench [han2022]
36_speech	3,686	400	1.65%	ODDS [rayana2016] / ADBench [han2022]
37_Stamps	340	9	9.12%	ODDS [rayana2016] / ADBench [han2022]
38_thyroid	3,772	6	2.47%	ODDS [rayana2016] / ADBench [han2022]
39_vertebral	240	6	12.5%	ODDS [rayana2016] / ADBench [han2022]
3_backdoor	95,329	196	2.44%	ODDS [rayana2016] / ADBench [han2022]
40_vowels	1,456	12	3.43%	ODDS [rayana2016] / ADBench [han2022]
41_Waveform	3,443	21	2.9%	ODDS [rayana2016] / ADBench [han2022]
42_WBC	223	9	4.48%	ODDS [rayana2016] / ADBench [han2022]
43_WDBC	367	30	2.72%	ODDS [rayana2016] / ADBench [han2022]
44_Wilt	4,819	5	5.33%	ODDS [rayana2016] / ADBench [han2022]
45_wine	129	13	7.75%	ODDS [rayana2016] / ADBench [han2022]
46_WPBC	198	33	23.74%	ODDS [rayana2016] / ADBench [han2022]
47_yeast	1,484	8	34.16%	ODDS [rayana2016] / ADBench [han2022]
48_chess	28,056	6	0.1%	ODDS [rayana2016] / ADBench [han2022]
49_kddcup99_prob	64,759	6	6.43%	ODDS [rayana2016] / ADBench [han2022]
4_breastw	683	9	34.99%	ODDS [rayana2016] / ADBench [han2022]
50_bank	41,188	10	11.27%	ODDS [rayana2016] / ADBench [han2022]
51_kddcup99_u2r	60,821	6	0.37%	ODDS [rayana2016] / ADBench [han2022]
5_campaign	41,188	62	11.27%	ODDS [rayana2016] / ADBench [han2022]
6_cardio	1,831	21	9.61%	ODDS [rayana2016] / ADBench [han2022]
7_Cardiotocography	2,114	21	22.04%	ODDS [rayana2016] / ADBench [han2022]
8_celeba	202,599	39	2.24%	ODDS [rayana2016] / ADBench [han2022]
9_census	299,285	500	6.2%	ODDS [rayana2016] / ADBench [han2022]

References

Full BibTeX for every source cited in the tables above.

@misc{uci,
  title        = {{UCI} Machine Learning Repository},
  author       = {Dua, Dheeru and Graff, Casey},
  year         = {2019},
  institution  = {University of California, Irvine, School of Information and Computer Sciences},
  url          = {https://archive.ics.uci.edu}
}

@article{openml,
  title   = {{OpenML}: Networked Science in Machine Learning},
  author  = {Vanschoren, Joaquin and van Rijn, Jan N. and Bischl, Bernd and Torgo, Luis},
  journal = {ACM SIGKDD Explorations Newsletter},
  volume  = {15},
  number  = {2},
  pages   = {49--60},
  year    = {2013}
}

@misc{delve,
  title        = {{DELVE}: Data for Evaluating Learning in Valid Experiments},
  author       = {Rasmussen, Carl Edward and Neal, Radford M. and Hinton, Geoffrey and van Camp, Drew and Revow, Michael and Ghahramani, Zoubin and Kustra, Rafal and Tibshirani, Robert},
  institution  = {University of Toronto, Department of Computer Science},
  url          = {https://www.cs.toronto.edu/~delve/}
}

@inproceedings{guyon2004,
  title     = {Result Analysis of the {NIPS} 2003 Feature Selection Challenge},
  author    = {Guyon, Isabelle and Gunn, Steve and Ben-Hur, Asa and Dror, Gideon},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {17},
  year      = {2004}
}

@article{lecun1998,
  title   = {Gradient-Based Learning Applied to Document Recognition},
  author  = {LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal = {Proceedings of the IEEE},
  volume  = {86},
  number  = {11},
  pages   = {2278--2324},
  year    = {1998}
}

@techreport{harries1999,
  title       = {{SPLICE-2} Comparative Evaluation: Electricity Pricing},
  author      = {Harries, Michael},
  institution = {University of New South Wales, School of Computer Science and Engineering},
  year        = {1999}
}

@article{souza2020,
  title   = {Challenges in Benchmarking Stream Learning Algorithms with Real-World Data},
  author  = {Souza, Vinicius M. A. and dos Reis, Denis M. and Maletzke, Andre G. and Batista, Gustavo E. A. P. A.},
  journal = {Data Mining and Knowledge Discovery},
  volume  = {34},
  pages   = {1805--1858},
  year    = {2020}
}

@misc{zhu2010,
  title  = {Stream Data Mining Repository},
  author = {Zhu, Xingquan},
  year   = {2010},
  url    = {https://www.cse.fau.edu/~xqzhu/stream.html}
}

@article{elwell2011,
  title   = {Incremental Learning of Concept Drift in Nonstationary Environments},
  author  = {Elwell, Ryan and Polikar, Robi},
  journal = {IEEE Transactions on Neural Networks},
  volume  = {22},
  number  = {10},
  pages   = {1517--1531},
  year    = {2011}
}

@inproceedings{street2001,
  title     = {A Streaming Ensemble Algorithm ({SEA}) for Large-Scale Classification},
  author    = {Street, W. Nick and Kim, YongSeog},
  booktitle = {Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages     = {377--382},
  year      = {2001}
}

@article{agrawal1993,
  title   = {Database Mining: A Performance Perspective},
  author  = {Agrawal, Rakesh and Imielinski, Tomasz and Swami, Arun},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  volume  = {5},
  number  = {6},
  pages   = {914--925},
  year    = {1993}
}

@article{bifet2010,
  title   = {{MOA}: Massive Online Analysis},
  author  = {Bifet, Albert and Holmes, Geoffrey and Kirkby, Richard and Pfahringer, Bernhard},
  journal = {Journal of Machine Learning Research},
  volume  = {11},
  pages   = {1601--1604},
  year    = {2010}
}

@article{montiel2021,
  title   = {River: Machine Learning for Streaming Data in Python},
  author  = {Montiel, Jacob and Halford, Max and Mastelini, Saulo Martiello and Bolmier, Geoffrey and Sourty, Raphael and Vaysse, Robin and Zouitine, Adil and Gomes, Heitor Murilo and Read, Jesse and Abdessalem, Talel and Bifet, Albert},
  journal = {Journal of Machine Learning Research},
  volume  = {22},
  number  = {110},
  pages   = {1--8},
  year    = {2021}
}

@article{friedman1991,
  title   = {Multivariate Adaptive Regression Splines},
  author  = {Friedman, Jerome H.},
  journal = {The Annals of Statistics},
  volume  = {19},
  number  = {1},
  pages   = {1--67},
  year    = {1991}
}

@article{pedregosa2011,
  title   = {Scikit-learn: Machine Learning in {P}ython},
  author  = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, {\'E}douard},
  journal = {Journal of Machine Learning Research},
  volume  = {12},
  pages   = {2825--2830},
  year    = {2011}
}

@article{katakis2010,
  title   = {Tracking Recurring Contexts Using Ensemble Classifiers: An Application to Email Filtering},
  author  = {Katakis, Ioannis and Tsoumakas, Grigorios and Vlahavas, Ioannis},
  journal = {Knowledge and Information Systems},
  volume  = {22},
  number  = {3},
  pages   = {371--391},
  year    = {2010}
}

@misc{rayana2016,
  title        = {{ODDS} Library},
  author       = {Rayana, Shebuti},
  year         = {2016},
  institution  = {Stony Brook University, Department of Computer Science},
  url          = {http://odds.cs.stonybrook.edu}
}

@inproceedings{han2022,
  title     = {{ADBench}: Anomaly Detection Benchmark},
  author    = {Han, Songqiao and Hu, Xiyang and Huang, Hailiang and Jiang, Mingqi and Zhao, Yue},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},
  volume    = {35},
  year      = {2022}
}