Feature selection algorithms: a survey and experimental evaluation

By Luis Carlos Molina, L. B. Muñoz, À. Nebot. IEEE International Conference on Data Mining. Proceedings, 2002

718 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/41355d17eb419a9d0abb627e86a5d5016f477bf0

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In Computer Vision and Pattern Recognition

Citations in the corpus

A stability index for feature selection (2007, background): “Feature selection has been a focus of pattern recognition for a long time now [13, 4, 9, 2, 3, 7, 12, 10, 11].”
On the Relationship Between Feature Selection and Classification Accuracy (2008, background): “Several extensive surveys of various feature selection and dimensionality reduction approaches can be found in the literature, for example, in Molina et al. (2002) or Guyon and Elisseeff (2003).”
Optimization of ECG Classification by Means of Feature Selection (2011, methodology): “Unless intrinsically embedded in the system (like weights in some ANN), the FS process can follow two schemes: ﬁlter and wrapper [47].”
Feature Selection: A literature Review (2014, methodology): “Other operators are restricted with some criterion such as the number of features or minimizing the error at each step [2].”
A review of feature selection methods on synthetic data (2013, methodology): “More experimental work on feature selection algorithms for comparative purposes can be found in [43–47], some of which were performed over artificially generated data, like the widely used Parity, LED or Monks problems [48].”
Optimal Value of Information in Graphical Models (2009, background): “The subset selection problem as an instance of feature selection is a central issue in machine learning, with a vast amount of literature (see Molina, Belanche, & Nebot, 2002 for a survey).”
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].”
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data (2013, background): “1 INTRODUCTION With the aim of choosing a subset of good features with respect to the target concepts, feature subset selection is an effective way for reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility [43], [46].”
Feature selection for high-dimensional data (2016, background): “[50] assessed the performance of fundamental feature selection algorithms in a controlled scenario, taking into account data set relevance, irrelevance and redundancy.”
Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, background): “See Chandrashekar and Sahin (2014), Guyon and Elisseeff (2003) and Molina et al. (2002) for a survey.”
A comprehensive survey on recent metaheuristics for feature selection (2022, background): “the amount of relevancy and redundancy of datasets [57].”
Recent advances and emerging challenges of feature selection in the context of big data (2015, background): “[52] assessed the performance of fundamental feature selection algorithms in a controlled scenario, taking into account dataset relevance, irrelevance and redundancy.”