UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences

By Arthur U. Asuncion. 2007

1,956 citations recorded by the source corpus.

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Citations in the corpus

Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, methodology): “In this section, we conduct experiments using two real datasets: the Adult dataset [39] and the Dutch census of 2001 [40].”
Differentially Private Empirical Risk Minimization (2009, methodology): “The first dataset we consider is the Adult dataset from the UCI Machine Learning Repository (Asuncion and Newman, 2007).”
UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, methodology): “For instance, the UCI Machine Learning Repository1 is a well-known example [7] in this sense.”
Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering (2017, background): “Name Origin Households LPs/Hh Resolution Dataport [32] US 707 647 15 min Redd [31] US 5 7 1 s AMPds [33] Canada 1 726 1 min ECO [34, 35] Switzerland 6 192 1 min UCI [36] France 1 1358 1 min GOVAU [37] Australia 31 406 30 min UMASS [38] US 376 1 1 min”
Deep Learning with Gaussian Differential Privacy (2019, methodology): “Originally from the UCI repository in Dua and Graff (2017), the Adult income dataset has been preprocessed into the LIBSVM format by Chang and Lin (2011).”
Fairness-Aware Classifier with Prejudice Remover Regularizer (2012, methodology): “test file of the Adult / Census Income distributed at the UCI Repository [7].”
Support vector machine classification with indefinite kernels (2007, methodology): “When too much noise is present, however, (for example, pixel data with range in [−1, 1] was modified with uniform noise in [−2, 2] before being normalized to [0, 1]), the performance of both techniques is comparable.”
Structure Discovery in Bayesian Networks by Sampling Partial Orders (2016, methodology): “The Flare, German, Mushroom, and Spambase data sets are obtained from the UCI Machine Learning Repository (Lichman, 2013).”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, methodology): “Besides the data sets created using the data sets generator, we also created partially artificial data sets based on the real-world databases Contraceptive, Yeast, Iris, and Car, from the UCI Machine Learning Repository [44] , by introducing simulated drifts inspired by Scholz and Klinkenberg [7].”