Stability of feature selection algorithms: a study on high-dimensional spaces

By Alexandros Kalousis, Julien Prados, Melanie Hilario. 2005

625 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability (2021, neutral): “Stability in our work speciﬁcally refers to intensional stability of feature selection method, which can be measured by the variability in the set of features selected [29,30].”
Robust Feature Selection Using Ensemble Feature Selection Techniques (2008, methodology): “Here, following [5], we take a similarity based approach where feature stability is measured by comparing the outputs of the feature selectors on the k subsamples.”
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods (2010, background): “As a linear SVM essentially consists of a separating hyperplane in the input space, the absolute values of the weights of each dimension in the hyperplane can be regarded as the contribution (importance) of each dimension (feature) to the multivariate decision of the hyperplane.”
A survey on feature selection methods (2014, methodology): “Stability of a feature selection algorithm can be viewed as the consistency of an algorithm to produce a consistent feature subset when new training samples are added or when some training samples are removed [68,70–73].”
Variable selection with error control: another look at stability selection (2011, methodology): “studied recently in the context of density estimation by Seregin and Wellner (2010) and Koenker and Mizera (2010); see also Dharmadhikari and Joag-Dev (1988).”
Feature Selection: A literature Review (2014, methodology): “Feature selection is one of the most frequent and important techniques in data preprocessing, and has become an indispensable component of the machine learning process [1].”
On The Stability of Interpretable Models (2018, methodology): “As pointed out by [16], intensional instability of feature selection does not necessarily implies extensional instability of the final classifier, due to redundant features.”
An experimental study of the intrinsic stability of random forest variable importance measures (2016, background): “With respect to data perturbation stability, the main focus is the consistence between feature rankings, each of which comes from different subsamples of a training set (e.g., 10-fold cross validation) [15, 19–21].”
Experimental analysis of new algorithms for learning ternary classifiers (2015, background): “Learning classifiers in very high dimension data has received more and more attention in the past ten years both theoretically [2], [3], [4], [5], [6], [7] and in practice through Data Mining applications [8], [9], [10], [11], [12], [13], [14], [15], [16], [17].”
Advances in variable selection methods I: Causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships (2012, neutral): “(4)), similarity index (Kalousis et al., 2007 Eq.”
Correlation and variable importance in random forests (2013, background): “This instability is a consequence of the data complexity in high dimensional settings (see Kalousis et al. [19], Křížek et al. [21]).”