Characterization of Classification Algorithms

By João Gama, Pavel Brazdil. 1995

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “, (Sohn, 1999; Gama and Brazdil, 2000).”
Automatic classifier selection for non-experts (2014, methodology): “Instead of such a classification approach, Gama and Brazdil [17] used regression in order to predict the performance values of different algorithms.”
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors (2008, methodology): “All these methods were already used in previous work as meta-regressors (see, for instance, [3]).”
Meta-learning for evolutionary parameter optimization of classifiers (2012, background): “2003), or predicting the performance of algorithms individually (Gama and Brazdil 1995; Sohn 1999; Köpf et al. 2000; Bensusan and Kalousis 2001).”
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, background): “The meta-learner is trained to map the meta-features to the performance [50], [99], [100], applicability [101], [102], or ranking [103], [104], [105], [106] of models.”
A Perspective View and Survey of Meta-Learning (2002, background): “Gama and Brazdil (1995) extract domain characteristics such as the number of examples, number of attributes, number of classes, standard deviation ratio for each feature, skewness, kurtosis, noise-signal ratio, etc., to generate metarule models.”
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results (2003, methodology): “In previous work where IBL was used for meta-learning, some positive (Gama & Brazdil, 1995; Lindner & Studer, 1999) and some negative (Kalousis & Hilario, 2000; Pfahringer, Bensusan, & Giraud-Carrier, 2000) results have been reported.”
Using Meta-Learning to Support Data Mining (2004, background): “Much work in dataset characterization has concentrated on extracting statistical and information-theoretic parameters estimated from the training set ([2],[21],[25],[31],[34],[46]).”
Theoretical Comparison between the Gini Index and Information Gain Criteria (2004, unknown): “experiments: [7, 1, 8, 12, 3, 19, 4, 5, 6].”
Estimating the Predictive Accuracy of a Classifier (2001, methodology): “The idea of using regression to predict the performance of learning algorithms was rst used by Gama and Brazdil in [ 7 ], were they continued on the framework adopted in STATLOG.”
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates (2017, background): “Thus, feature extractors are available for many problems, including mixed integer programming (MIP; Leyton-Brown et al 2009; Kadioglu et al 2010; Xu et al 2011; Hutter et al 2014b), propositional satisfiability (SAT; Nudelman et al 2004; Xu et al 2008; Hutter et al 2014b), answer set programming (AS”
Introduction to the Special Issue on Meta-Learning (2004, background): “…a set of domain characteristics or meta-features that are relevant for predicting the performance of the given learning algorithms (Aha, 1992; Michie, Spiegelhalter, & Taylor, 1994; Gama & Brazdil, 1995; Brazdil, 1998; Keller, Paterson, & Berrer, 2000; Brazdil, Soares, & Pinto da Costa, 2003).”