On learning algorithm selection for classification

By Shawkat Ali, K. Smith‐Miles. Applied Soft Computing, 2006

413 citations recorded by the source corpus.

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A meta-learning approach to automatic kernel selection for support vector machines (2006, background): “We have already started to investigate this direction for comparing the suitability of different learning algorithms on different data sets, including neural networks, SVMs, and other machine learning methods [2].”
Meta-Learning: A Survey (2018, neutral): “Gravity gravity(X) Inter-class dispersion [5]”
Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification (2013, background): “The difficulty of solving such problem mathematically lies in the accuracy and distribution of data properties and model capabilities [19].”
Automatic classifier selection for non-experts (2014, methodology): “[2] used a rule learner based on meta-features to predict the most suitable classifier.”
Meta-learning for evolutionary parameter optimization of classifiers (2012, methodology): “…applicability of different classification algorithms on a dataset, either by predicting only the best algorithm (Bensusan and Giraud- Carrier 2000a; Ali and Smith 2006), predicting a ranking of multiple algorithms (Merz 1995; Berrer et al. 2000; Brazdil et al. 2003), or predicting the performance…”