Wrappers for Feature Subset Selection

By Ron Kohavi, George H. John. 1997

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Citations in the corpus

Adversarial Feature Selection Against Evasion Attacks (2016, methodology): “It is a simple variant of the popular forward selection and backward elimination wrapping algorithms, which iteratively add or delete a feature from the current candidate set, starting, respectively, from an empty feature set and from the full feature set [38], [44], [58].”
Data preprocessing techniques for classification without discrimination (2011, methodology): “[8] also utilize a wrapper [16] approach to determine the percentage of minority class examples to be added to the training set and the percentage to under-sample the majority class examples.”
Methods and Models for Interpretable Linear Classification (2014, background): “The interpretability of transparent models is usually improved by tuning sparsity [107, 120, 29, 50, 89, 19, 59, 47], by ensuring monotonic relationships between certain input variables and the predicted outcome [10, 87, 114, 77], and by restricting coefficients to a small set of integer values [23,”
WikiRelate! Computing Semantic Relatedness Using Wikipedia (2006, methodology): “For all feature sets we determine the relevant features following an iterative procedure similar to the wrapper approach for feature selection (Kohavi & John, 1997).”
Feature Selection for Classification: A Review (2014, methodology): “Based on this assumption, wrapper models utilize a specific classifier to evaluate the quality of selected features, and offer a simple and powerful way to address the problem of feature selection, regardless of the chosen learning machine [36, 26].”
Subspace clustering for high dimensional data: a review (2004, methodology): “This is not feasible because the subset generation problem is intractable [49; 14].”
k-Nearest Neighbour Classifiers (2007, methodology): “This is overcome in the Wrapper approach by using the performance of the classifier to guide search in feature selection – the classifier is wrapped in the feature selection process [11].”
Laplacian Score for Feature Selection (2005, methodology): “Feature selection methods can be classified into “wrapper” m thods and “filter” methods [4].”
Feature Selection for SVMs (2000, methodology): “Wrapper methods can provide more accurate solutions than filter methods [9], but in general are more computationally expensive since the induction algorithm 9GAFC must be evaluated over each feature set (vector ) considered, typically using performance on a hold out set as a measure of goodness of f”
Toward Automatic Time-Series Forecasting Using Neural Networks (2012, methodology): “Broadly speaking, feature selection methods can be categorized into filter and wrapper approaches [56].”
Predictive Modeling With Big Data: Is Bigger Really Better? (2013, unknown): “…been argued that sampling (reducing the number of instances) or transformation of the data to lower dimensional spaces (reducing the number of features) is beneﬁcial, 22 whereas others have argued that massive data can lead to lower estimation variance and therefore better predictive performance.”
Data analysis for electronic nose systems (2006, methodology): “The feature subset selection algorithm conducts a search for a good subset using the classifier as part of the evaluation function [38].”
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework (2007, methodology): “Feature selection algorithms may be widely categorized into two groups: filter and wrapper methods [2, 4-10 ].”