Automatic classifier selection for non-experts

By Matthias Reif, Faisal Shafait, Markus Goldstein, Thomas Breuel, Andreas R. Dengel. 2014

163 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neural Architecture Search & AutoML

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In Neural Architecture Search & AutoML

Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Moreover, they can also be used to predict the performance of new task based on a particular configuration [105, 45].”
A Survey on Neural Architecture Search (2019, background): “properties of the dataset, also called meta-features (Reif et al., 2014), are considered.”
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates (2015, methodology): “2008), regression models have been extensively used to predict the performance of algorithms across various datasets based on dataset features (Guerra, Prudêncio, and Ludermir 2008; Reif et al. 2014).”
Meta-Learning: A Survey (2018, neutral): “More complete surveys can be found in the literature [26, 98, 130, 138, 175].”
Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data (2016, methodology): “While meta-learning is most often applied for selecting classification algorithms [12], [13], [14], [15], fewer applications exist for selecting models for univariate [16], [17], [18], [19] or multivariate time series forecasting [20], [21].”
On the predictive power of meta-features in OpenML (2017, background): “In the work of Reif et al. (2014), an empirical evaluation of different categories of meta-features in the context of their suitability for predicting classification accuracies for a number of standard classifiers can be found.”
Surrogate Benchmarks for Hyperparameter Optimization (2014, methodology): “In the field of metalearning [6], regression models have been extensively used to predict the performance of algorithms across various datasets based on dataset features [11, 26].”