Input feature selection for classification problems

By Nojun Kwak, Chong-Ho Choi. IEEE Trans. Neural Networks, 2002

988 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6a0352b285fd65e02f7746135806097be2dbd030

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Citations in the corpus

Robust Sparse Linear Discriminant Analysis (2019, background): “FEATURE selection and extraction play important roles in pattern classification and have received much attention in recent years [1].”
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, background): “Experiments show irrelative attributes will deteriorate the performance of the learning algorithms for the curse of dimensionality, increase training and test times [2] [3].”
Group sparse regularization for deep neural networks (2016, background): “of the input vector), including principal component analysis, mutual information [18], autoencoders, and many others.”
A review of feature selection methods based on mutual information (2013, background): “An alternative definition of relevance is given under the framework of mutual information [6, 21, 32, 33, 37, 53, 55, 67].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “The most common approach to developing expressive and human-readable representations of knowledge is the use of if-then production rules [192].”
Input Feature Selection by Mutual Information Based on Parzen Window (2002, methodology): “Because the computation of mutual information between continuous variables is a very difficult job requiring probability density functions (pdf) and involving integration of those functions, mutual information feature selector (MIFS) [2], and its variants [3], [4] used histograms in approximating th”
Genetic programming for simultaneous feature selection and classifier design (2006, methodology): “Table IX shows that performs much better than ADHOC and the four mutual information based selection schemes [5].”
Differential evolution for filter feature selection based on information theory and feature ranking (2018, background): “Other representative examples of mutual information based approaches are maximum relevance and minimum redundancy (mRmR) [14] , uniformly improved MIFS (MIFS-U) [15] , and conditional mutual information maximization (CMIM) [16] .”
Tree‐based iterative input variable selection for hydrological modeling (2013, background): “The main drawback of this approach stands in its computational requirements [Kwak and Choi, 2002; Chow and Huang, 2005], as a large number of calibration and validation processes must be performed to single out the best combination of inputs and so the method does not scale well to a large data set.”
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “We have also included the results with the value of b equal to 1, which is the same as the value applied in [34].”
From explanations to feature selection: assessing SHAP values as feature selection mechanism (2020, methodology): “Thus, we tested SHAP against three common feature selection approaches: Mutual Information [35]–[37], Recursive Feature Elimination [31], and ANOVA [38].”
An Effective Feature Selection Method via Mutual Information Estimation (2012, background): “Several variants of criterion (8) have been proposed in the literature [3], [8], [9] by modifying the second term in (8).”
A hybrid genetic algorithm for feature selection wrapper based on mutual information (2007, methodology): “The inherent error exiting in the process of conversion from continuous variables to discrete ones is bounded by some constant values depending only on the number of partitions that divide the continuous space (Kwak and Choi, 2002; Fraser and Swinney, 1986).”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “The main target of feature selection is to choose relevant features from a large feature set [24,17].”
Information-preserving hybrid data reduction based on fuzzy-rough techniques (2006, methodology): “Keywords: Attribute reduction; Hybrid data; Fuzzy-rough set; Information measure”
MIFS-ND: A mutual information-based feature selection method (2014, methodology): “Feature selection has also been applied as a preprocessing task in clustering gene expression data in search of co-expressed patterns.”