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God doesn't always shave with Occam's razor — Learning when and how to prune
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-57868-4_52openalex-snapshot
Characterizing the applicability of classification algorithms using meta-level learning
inferredcites →https://doi.org/10.1007/3-540-62858-4_76openalex-snapshot
Conditions for Occam's razor applicability and noise elimination
inferredcites →https://doi.org/10.1023/a:1022604100933openalex-snapshot
An Empirical Comparison of Pruning Methods for Decision Tree Induction
inferredcites →https://doi.org/10.1613/jair.228openalex-snapshot
Further Experimental Evidence against the Utility of Occam's Razor
inferredcites →https://doi.org/10.1613/jair.41openalex-snapshot
Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Improved Dataset Characterisation for Meta-learning
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Discovering Task Neighbourhoods through Landmark Learning Performances
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Meta-Learning - Concepts and Techniques
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The Need for Interpretability Biases
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Dataset Characteristics (Metafeatures)
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Shorter Rules Are Better, Aren’t They?
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A Landmarker Selection Algorithm Based on Correlation and Efficiency Criteria
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Classifier selection using geometry preserving feature
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An improved data characterization method and its application in classification algorithm recommendation
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On cognitive preferences and the plausibility of rule-based models
inferred← citeshttps://doi.org/10.1016/j.knosys.2018.07.015openalex-snapshot
A new classification algorithm recommendation method based on link prediction
inferred← citeshttps://doi.org/10.1016/j.knosys.2021.106933openalex-snapshot
Ensemble of ML-KNN for classification algorithm recommendation
inferred← citeshttps://doi.org/10.1023/b:mach.0000015878.60765.42openalex-snapshot
Introduction to the Special Issue on Meta-Learning
inferred← citeshttps://doi.org/10.1109/ichis.2004.33openalex-snapshot
Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation
inferred← citeshttps://doi.org/10.1109/icmla.2004.1383511openalex-snapshot
A new landmarker generation algorithm based on correlativity
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LEONARDO - The computational intelligence (CI) model selection wizard
inferred← citeshttps://doi.org/10.1109/icmla.2007.66openalex-snapshot
Using genetic programming for the induction of oblique decision trees
inferred← citeshttps://doi.org/10.1109/jproc.2013.2274907openalex-snapshot
The Reasonable Ineffectiveness of Mathematics [Point of View]
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3366615openalex-snapshot
Meta-Scaler: A Meta-Learning Framework for the Selection of Scaling Techniques
inferred← citeshttps://doi.org/10.1111/j.0030-1299.2007.15534.xopenalex-snapshot
Reconsidering the mechanistic basis of the metabolic theory of ecology
inferred← citeshttps://doi.org/10.1145/2629474openalex-snapshot
A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method
inferred← citeshttps://doi.org/10.11606/t.55.2010.tde-04012011-142551openalex-snapshot
Meta-aprendizagem aplicada à classificação de dados de expressão gênica
inferred← citeshttps://doi.org/10.11606/t.55.2013.tde-26032014-155351openalex-snapshot
Meta-aprendizado aplicado a fluxos contínuos de dados
inferred← citeshttps://doi.org/10.1177/2053951720926558openalex-snapshot
The virtue of simplicity: On machine learning models in algorithmic trading
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The Reasonable Ineffectiveness of Mathematics
inferred← citeshttps://doi.org/10.1561/2200000093openalex-snapshot
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
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Physiological constraints on the ecology of activity-limited ectotherms
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Automatic selection of classification learning algorithms for data mining practitioners
inferred← citeshttps://doi.org/10.48550/arxiv.2101.05993openalex-snapshot
Ensemble Learning Based Classification Algorithm Recommendation
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