Feature Selection for Classification: A Review

By Jiliang Tang, Salem Alelyani, Huan Liu. Data Classification: Algorithms and Applications, 2014

1,235 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ce2fa0dbf76d20369f793c11d2d8803880a9ab2d

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Citations in the corpus

A review of unsupervised feature selection methods (2019, methodology): “Over the last decades, many feature selection methods have been proposed, most of them developed for supervised classification tasks (Tang et al. 2014).”
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “This study proposes a data selection algorithm, namely, the spatio-temporal feature selection algorithm, which is based on the Wrapper models (Tang, Alelyani, and Liu 2014).”
Relief-Based Feature Selection: Introduction and Review (2017, methodology): “This list is an updated version of one regularly used in the literature (Dash and Liu, 1997; Mlambo et al., 2016; Tang et al., 2014; Gore and Govindaraju, 2016).”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, unknown): “considered in wrapper approaches category [17].”
Bandit-Based Automated Machine Learning (2018, methodology): “As preprocessing, we choose Feature Selection (FS) and Feature Extraction (FE) methods, because data with extremely high dimensionality has presented one of the most prominent challenges to existing learning methods [13], and these are the most widely employed techniques for reducing dimensionality ”
Recent advances in feature selection and its applications (2017, methodology): “The detailed summary for group structure, tree structure, and graph structure information used in the sparsity-based feature selection is presented in [19].”
OvA-INN: Continual Learning with Invertible Neural Networks (2020, background): “Feature Selection or Feature Aggregation techniques may help to alleviate this issue [34].”
Feature Selection: A literature Review (2014, methodology): “To solve this problem, some methods have tried to overcome by memorizing only important samples or summaries [129].”
Feature selection for text classification: A review (2018, methodology): “The wrapper approach achieves better classification accuracy than filter methods, however the computational complexity of wrapper approaches is very high [66, 89].”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, background): “The general process is: select a subset, evaluate the subset according to the prediction performance, select a new subset, and continue to evaluate until the expected quality is reached [75].”
A review of feature selection methods with applications (2015, methodology): “Minimum redundancy, maximum relevance (mRmR) multivariate, information classification, regression [2]”
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017, background): “) should also preserve feature structures in the original feature space since those structures play important roles in many real-world applications such as natural language processing and bioinformatics [Mitra et al., 2002; Tang et al., 2014].”
A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “Tang et al. prepared a survey about the most-used feature selection algorithms: Linear Discriminant Analysis,115 Principal Component Analysis, Information Gain, Canonical Correlation Analysis, Relief, Lasso, and Fisher Score [67].”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, background): “FS algorithms can be separated into three categories [25]:”