Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

By Sanmay Das. International Conference on Machine Learning, 2001

711 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/93b625a0e35b59fa6a3e7dc1cbdb31268d62d69f

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Citations in the corpus

Data Preprocessing for Supervised Leaning (2007, methodology): “BDSFS (Boosted Decision Stump FS) uses boosted decision stumps as the pre-processor to discover the feature subset [ 9 ].”
Consistency-based search in feature selection (2003, background): “Recent papers such as [2] [9] [16] [17] [22] [43] address some of the existing issues of feature selection.”
Character Recognition Systems: A Guide for Students and Practitioners (2007, methodology): “Other papers worthy of mention include [15, 20, 99].”
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (2003, methodology): “Feature selection algorithms fall into two broad categories, the filter model or the wrapper model (Das, 2001; Kohavi & John, 1997).”
Feature Selection for Clustering: A Review (2018, methodology): “Due to these shortcomings in each model, the hybrid model [13, 40], was proposed to bridge the gap between the filter and wrapper models.”
Toward integrating feature selection algorithms for classification and clustering (2005, methodology): “Feature selection algorithms designed with different evaluation criteria broadly fall into three categories: the filter model [17], [34], [59], [95], the wrapper model [13], [27], [42], [44], and the hybrid model [15], [68], [91].”
A review of feature selection methods with applications (2015, methodology): “Therefore, for a reliable generalization error estimate, it is necessary that both an independent validation sample and another modelling algorithm are used after the final subset is found.”
High-performing feature selection for text classification (2002, methodology): “While over 100 method variations have been tested (as described above), only the top 3 for each classifier are included, sorted by average performance over the [1-25] percent range.”
Tree‐based iterative input variable selection for hydrological modeling (2013, background): “In such situations, forward selection is far less time consuming than backward elimination [Das, 2001].”
Feature selection for text classification: A review (2018, methodology): “Depending on how to generate feature subsets, feature selection methods can be divided into four categories, namely, filter model [10, 83, 110], wrapper model [24, 71], embedded model, and hybrid model [18, 104].”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, methodology): “In the classification phase, a point x is assigned a label y according to y = sgn[w x + b].”
Trace Ratio Criterion for Feature Selection (2008, background): “These feature selection algorithms can also be categorized into wrappers and filters (Kohavi & John 1997; Das 2001).”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “Feature selection algorithms are separated into three categories [10, 11]: Multivariate methods are able to find relationships among the features, while univariate methods consider each feature separately.”
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data (2013, methodology): “The ﬁlter methods are independent of learning algorithms, with good generality.”
Feature Selection: A literature Review (2014, methodology): “Filter + Wrapper BBHFS [82], Xing’s [91] Dash-Liu’s [81]”