A survey on feature selection methods

By Girish Chandrashekar, Ferat Sahin. 2014

4,798 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Auditing black-box models for indirect influence (2016, background): “From a technical standpoint (see [7]), the wrapper approach to feature selection (in which features are evaluated based on the quality of the resulting prediction) is most related to our work.”
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, background): “There exist a range of technical approaches to derive each form of explanation, whether it be learning sparse models [Mehmood et al., 2012, Chandrashekar and Sahin, 2014], monotone functions [Canini et al., 2016], or efﬁcient logic-based models [Rivest, 1987].”
Automated Machine Learning: From Principles to Practices (2018, methodology): “The prediction can be achieved by traditional curve fitting methods [34] or training a surrogate model for prediction [7, 73].”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, background): “However, due to the high number of computations required to generate the feature subsets and evaluate them, wrapper methods are computationally heavy (relative to ﬁ lter and embedded methods) (Chandrashekar and Sahin, 2014).”
Feature dimensionality reduction: a review (2022, background): “Feature selection can also be called variable selection or feature subset selection, and it is a process of selecting feature subsets that are applied to model construction [12].”
Shapley values for feature selection: The good, the bad, and the axioms (2021, neutral): “There are two prominent categories of feature selection techniques identified by the surveys of [51], [52]:”
Relief-Based Feature Selection: Introduction and Review (2017, methodology): “For reviews of features selection methods in general, we refer readers to (Langley, 1994; Dash and Liu, 1997; Guyon and Elisseeﬀ, 2003; Belanche and Gonz´alez, 2011; Bol´on-Canedo et al., 2013; Tang et al., 2014; Chandrashekar and Sahin, 2014; Jovi´c et al., 2015; Mlambo et al., 2016).”
Big data for cyber physical systems in industry 4.0: a survey (2019, methodology): “Besides research on speci ﬁ c prediction models, feature selection (Chandrashekar and Sahin 2014; Khalid, Khalil, and Nasreen 2014) and ensemble (Kuncheva and Rodríguez 2014) are two general techniques can be applied to any prediction models to improve the prediction performance.”
A feature selection model based on genetic rank aggregation for text sentiment classification (2017, methodology): “Feature selection methods can be broadly divided into two groups: filter-based and wrapper-based feature selection methods [23].”
Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML (2016, neutral): “The merits of and methods for feature selection are discussed extensively in a number of classical survey papers, hence we will keep the overview brief here [1,4,5,6,10].”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “Reducing the feature space can affect the size of the used dataset, the computational time, and the classification performance of several algorithms (Chandrashekar and Sahin, 2014).”