Feature Selection for Classification

By Manoranjan Dash, Huan Liu. 1997

Subfields: Evaluation & Benchmarks, Statistical Learning Theory

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Citations in the corpus

AutoML: A Survey of the State-of-the-Art (2019, background): “According to [83], there are four basic steps in a typical process of feature selection (see Figure 3), as follows: S E E E E E E E E M M M M M M M”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “mated feature selection [17] methods were soon available.”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “A simple domain-agnostic filtering approach for feature selection is based on information theory and statistics (Pudil et al., 1994; Yang and Pedersen, 1997; Dash and Liu, 1997; Guyon and Elisseeff, 2003).”
Survey on Automated Machine Learning (2019, background): “By examining the features from an information theoretic or statistics viewpoint (Pudil et al., 1994; Yang and Pedersen, 1997; Dash and Liu, 1997; Guyon and Elisseeff, 2003), no domain knowledge is necessary for feature selection.”
A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “2 can be seen in [1] and a typical iterative evolutionary workflow of feature selection can be seen in [18].”
Toward Automatic Time-Series Forecasting Using Neural Networks (2012, background): “Dash and Liu [55] have carried out a comprehensive overview of feature selection”
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, background): “It mainly measures the feature characteristics depending on four different kinds of measurement criteria, i.e. information, dependency, consistency, and distance [32].”
Pruning and Quantization for Deep Neural Network Acceleration: A Survey (2021, background): “Feature selection [52] is a technique that selects a subset of relevant features that contribute to the result.”
A literature review on the application of evolutionary computing to credit scoring (2013, background): “As a result of the opacity property of the support vector machines, they can sometimes become difﬁcult to apply to credit risk evaluation.”
Character Recognition Systems: A Guide for Students and Practitioners (2007, background): “[21] extracted baselines from bank checks by searching for a pair of opposite edges in a predefined distance.”
An advanced ACO algorithm for feature subset selection (2015, background): “Feature selection algorithms have been reviewed in [13, 20, 9].”
PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES (2004, background): “In addition, because FTOFs did not add accuracy and the run time of inductive algorithms grows with the number of attributes, we will examine if the selection of a subset of attributes (Dash and Liu 1997) could be useful.”
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data (2013, background): “However, the generality of the selected features is limited and the computational complexity is large.”
CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques (2021, methodology): “For the significant identification performance results, the input dataset keeps an important role by using the ML technique [17].”
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (2003, background): “…since 1970's and proven to be effective in removing irrelevant and redundant features , increasing efficiency in learning tasks, improving learning performance like predictive accuracy, and enhancing comprehensibility of learned results (Blum & Langley, 1997; Dash & Liu, 1997; Kohavi & John, 1997).”
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, methodology): “This brings about such challenges as managing computational time complexity while extracting compact yet effective models.”
Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, methodology): “According to the availability of class labels, there are feature selection methods for supervised learning (Dash and Liu, 1997; Yu and Liu, 2003) as well as for unsupervised learning (Kim et al.”