A stability index for feature selection

By Ludmila I. Kuncheva. 2007

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Citations in the corpus

Is Feature Selection Secure against Training Data Poisoning? (2015, methodology): “Furthermore, to understand how feature selection and ranking are affected by the attack, we also evaluate the consistency index originally deﬁned by Kuncheva (2007) to evaluate the stability of feature selection under random perturbations of the training data.”
An experimental study of the intrinsic stability of random forest variable importance measures (2016, methodology): “There are a few evaluation criteria aiming to measure the VIM stability [33–36].”
Ensembles for feature selection: A review and future trends (2019, unknown): “In [15, 16], some metrics for stability are discussed, and in [9] others for diversity are employed over rankers.”
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods (2010, methodology): “Ensemble method ideas have recently been ported to wide data (Dettling, 2004; Long and Berlian Vega, 2003) and in particular to feature selection (Saeys et al., 2008).”
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, background): “(5)), and the consistency index (Kuncheva, 2007 Eq.”
Variable selection with error control: another look at stability selection (2011, methodology): “Keywords: Complementary pairs stability selection; r -concavity; Subagging; Subsampling; Variable selection”
An empirical study on software defect prediction with a simplified metric set (2014, methodology): “5 is often used to measure the stability of feature selection algorithms [23].”
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection (2012, methodology): “To account FEATURE SELECTION VIA CONDITIONAL LIKELIHOOD for this, Kuncheva (2007) presents a consistency index, based on the hypergeometric distribution with a correction for chance.”
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, methodology): “Index, which provides a measure of uncertainty over the input variable selection procedure [Kuncheva, 2007].”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, unknown): “[40] for similarity measures; Kuncheva Index (KI) [41]”
On the Stability of Feature Selection Algorithms (2017, methodology): “We note that this is the assumption (sometimes implicitly) made by different authors using the concept of correction for chance, e.g. Kuncheva (2007); Lustgarten et al. (2009); Zhang et al. (2009); Guzmán-Mart́ınez and Alaiz-Rodŕıguez (2011).”
Partially supervised feature selection with regularized linear models (2009, background): “Kuncheva (2007) proposed such a stability index: where n is the total number of features, and S i , S j are two signatures built from diﬀerent subsets of the training samples.”
A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance (2015, methodology): “Deﬁnition 2.1 (Consistency [8]): The consistency index for two subsets A ⊂ X and B ⊂ X , such that r = | A ∩ B | and”
Robust Feature Selection Using Ensemble Feature Selection Techniques (2008, methodology): “Recent work in this area mainly focuses on the stability indices to be used for feature selection, introducing measures based on Hamming distance [4], correlation coefficients [5], consistency [6] and information theory [7].”