UCI Repository of machine learning databases

By Catherine Blake. 1998

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In Evaluation & Benchmarks

Citations in the corpus

Interpreting tree ensembles with inTrees (2014, methodology): “ simpliﬁed tree ensemble learner (STEL) implemented in the “inTrees” package version 1.0, we compare it to the popularly used decision tree method in the rpart package [4, 14] on 20 data sets from UCI[2]. Random forest with 100 trees was used. Conditions with maximum 14 Table 8: Top 10 most frequent”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, methodology): “We also test our methods on a larger semisynthetic SCM inspired by the German Credit UCI dataset [34].”
Mathematical optimization in classification and regression trees (2021, methodology): “Figure 2 depicts the tree model for carevaluations , a real-world dataset from the UCI Machine Learning repository (Blake and Merz 1998) with N = 1728 car evaluations divided into K = 4 classes.”
MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules (2018, methodology): “We conducted extensive experiments on publicly available data sets obtained from UCI repository [6].”
Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “Clearly, these strategies are of diverse nature and, as the computational experience will illustrate, o er di erent tradeo s between performance and reduction in computational cost.”
A Kernel Method for the Two-Sample-Problem (2006, methodology): “sets: the census income dataset from the UCI KDD archive (CNUM), the protein homology dataset from the 2004 KDD Cup (BIO) (Caruana and Joachims, 2004), and the forest dataset from the UCI ML archive (Blake and Merz, 1998). For the ﬁnal dataset, we performed univariate matching of attributes (FOREST)”
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2010, background): “These positive results not only encourage wide use of the proposed method in other Web-baesd applications, but also suggest a promising solution to create benchmark datasets from real-world applications for bandit algorithms.”
Expectation Propagation for approximate Bayesian inference (2001, methodology): “Figure 4 compares the error rate of EP, Billiard, and SVM on four datasets from the UCI repository (Blake & Merz, 1998).”
SMOTE: Synthetic Minority Over-sampling Technique (2002, methodology): “3. The Adult dataset ( Blake & Merz, 1998 )has 48,842 samples with 11,687 samples belonging to the minority class.”
The offset tree for learning with partial labels (2008, methodology): “We performed the comparison on a number of publicly available multiclass datasets [ 16 ].”
Finding a short and accurate decision rule in disjunctive normal form by exhaustive search (2010, background): “Many different approaches to learn decision rules have been developed (Mitchell 1997).”
Variable Selection for Clustering with Gaussian Mixture Models (2009, methodology): “i : ( 5 ) 4.2 The backward stepwise selection algorithm”
PERT – Perfect Random Tree Ensembles (2001, methodology): “Most of the datasets are available from the UCI repository (Murphy and Aha, 1994).”