In addition to accuracy, stability, having not too significant changes in the selected features when the identity of samples change, is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high.
In this study, we introduce a stability measure, and perform both accuracy and stability measurements of MRMR (Minimum Redundancy Maximum Relevance) feature ...
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High stability of the feature selection algorithm is equally important as the high classification accuracy when evaluating feature selection performance. In ...
Feb 22, 2021 · In conclusion, both accuracy and stability of results and of feature selector should be accounted for when constructing a model for prediction.
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Feature Selection is central to modern data science, from exploratory data analysis to predictive model-building. The “stability” of a feature selection ...
(PDF) Stable and Accurate Feature Selection - ResearchGate
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In addition to accuracy, stability is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number ...
Another important property of a feature selection method is stability that refers to robustness of the selected features to perturbations in the data. In ...
Sep 24, 2021 · The stability of feature selection refers to the insensitivity of feature selection results to changes in the training set. Highly stable ...
Sep 10, 2009 · Accurate feature selection methods are necessary so that good classification is possible using a smaller number of features.
Sep 1, 2024 · This work compares some of the most representative algorithms from different feature selection groups regarding a broad range of measures, several datasets, ...