STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS

By Ross D. King, Cao Feng, A. Sutherland. 1995

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Hyperparameter optimization (HPO) has a long history under the framework of AutoML [339, 237, 241], but it mainly concerns how to improve the model accuracy [59, 364, 321, 159] rather than communication and computing efficacy for”
Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, methodology): “The history of HPO dates back to the early 1990s (Ripley, 1993; King et al., 1995),and the method is widely applied for neural networks with the increasing use of machine learning.”
An empirical comparison of supervised learning algorithms (2006, background): “STATLOG is perhaps the best known study (King et al., 1995).”
Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease (2008, background): “King, Feng, and Sutherland (1995) compared symbolic learning (CART, C4.5, NewID, AC2, ITrule, Cal5, and CN2), statistics (Naı̈ve Bayes, k-nearest neighbor, kernel density, linear discriminant, quadratic discriminant, LR, projection pursuit, and Bayesian networks), and neural networks…”
Automatic classifier selection for non-experts (2014, methodology): “This approach was later extended by using more features and a Decision Tree learner within the well known StatLog project [23].”
Data mining in metric space: an empirical analysis of supervised learning performance criteria (2004, methodology): “The STATLOG project [6] performed a large scale empirical evaluation of a number of learning algorithms in 1995.”
Meta-learning for evolutionary parameter optimization of classifiers (2012, background): “Statistical meta-features (Gama and Brazdil 1995; Vilalta et al. 2004; Brazdil et al. 1994; King et al. 1995): – cancor1: canonical correlation for the best single combination of features – cancor2: canonical correlation for the best single combination of features orthogonal to cancor1 – fract1:…”
An empirical evaluation of supervised learning in high dimensions (2008, unknown): “STATLOG (King et al., 1995) was another comprehensive empirical study that was discussed in Section 1.”
Emotion in Human-Computer Interaction (2012, unknown): “[27], [19], [27].”
Tree Induction vs. Logistic Regression: A Learning-Curve Analysis (2003, unknown): “Notably, the Statlog project (King, Feng, and Sutherland, 1995) looked specifically at the characteristics of the data that indicate the preferability of different types of induction algorithms.”
Emotion recognition based on physiological changes in music listening (2008, background): “[34], for example, this 2072 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.”
Advances in Instance Selection for Instance-Based Learning Algorithms (2002, result): “Disregarding this problem, the classification scheme is simple and very effective in comparison to other methods such as feed-forward neural networks and decision trees (King et al., 1995).”
Beyond Manual Tuning of Hyperparameters (2015, methodology): “Several such meta-datasets have been collected and released, either as standalone datasets [35, 52] or implicitly though machine learning experiment databases such as OpenML [10, 59, 68].”
Inductive Conformal Prediction: Theory and Application to Neural Networks (2008, methodology): “The results of the k -Nearest Neighbours algorithm were produced by the author, while those of all other methods were reported in (King et al., 1995) and (Michie et al., 1994).”