Robust Feature Selection Using Ensemble Feature Selection Techniques

By Yvan Saeys, Thomas Abeel, Y. Van de Peer. ECML/PKDD, 2008

681 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1816bf2acc432656bdc435c1967f84ed8999fe13

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Citations in the corpus

Recent advances in feature selection and its applications (2017, methodology): “The stability [26–28].”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, background): “Stability and reproducibility of results is important because it increase the con ﬁ dence of users when inferring knowledge from the selected features (Saeys et al., 2008).”
A review of feature selection methods on synthetic data (2013, methodology): “Therefore, new feature selection methods are constantly appearing using different strategies: a) combining several feature selection methods, which could be done by using algorithms from the same approach, such as two filters [16], or coordinating algorithms from two different approaches, usually fi”
Variable selection with error control: another look at stability selection (2011, background): “Keywords: Complementary pairs stability selection; r -concavity; Subagging; Subsampling; Variable selection”
Feature Selection (2016, methodology): “It is defined as the sensitivity of a feature selection algorithm to perturbation in the training data [Kalousis et al. 2007; He and Yu 2010; Saeys et al. 2008; Loscalzo et al. 2009; Yang and Mao 2011].”
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis (2012, methodology): “In [57], the overall stability is defined in general terms as the average over all pair-wise comparisons between different signatures”
On Oblique Random Forests (2011, background): “More recently, additional properties of the random forest have gained interest, for example in feature selection [25, 34, 23, 44] or the explorative analysis of sample proximities [38].”
Relief-Based Feature Selection: Introduction and Review (2017, methodology): “Other notable Relief methodological variations include approaches for feature set evaluation (Arauzo-Azofra et al., 2004), instance selection (Dash and Yee, 2007), and ensemble learning (Saeys et al., 2008; Zhou et al., 2014).”
Ensembles for feature selection: A review and future trends (2019, neutral): “The rationale for the use of ensembles in the feature selection process is thus clear [7], as by generating many predictors, the solution space can be massively explored and by later combining all individual results, the ensemble is able to re(cid:13)ect this exploration, so as to obtain a more…”
Rough Sets: Selected Methods and Applications in Management and Engineering (2012, methodology): “Rough sets were also used for marketing decision support systems in Swedish retail business [51], tourism shopping [52], business failure prediction [53], predicting insolvency insurance companies [54] and business failure prediction in Greece [55].”
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods (2010, background): “Such models also offer state-of-the-art classification performance on a wide range of applications (Schoelkopf and Smola, 2002).”
Feature Selection: A literature Review (2014, methodology): “ To ensure a better behavior of feature selection using an ensemble method [84, 85]”
An up-to-date comparison of state-of-the-art classification algorithms (2017, background): “Saeys, Abeel, and de Peer (2008) investigated on high-dimensional data, the use of ensemble of multiple feature selection methods for improving classiﬁcation stability and accuracy.”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “[106] also applied random forests as an embedded feature selection algorithm.”
On the Stability of Feature Selection Algorithms (2017, methodology): “For example, Saeys et al. (2008) conclude that “RELIEF is one of the less stable algorithms” and “Random Forests clearly outperform other feature selection methods regarding robustness”.”