Comparison of algorithms that select features for pattern classifiers

By Mineichi Kudo, J. Sklansky. Pattern Recognition, 2000

965 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b233f730f07ecc00478719582f7409bd9598702c

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Citations in the corpus

Review of soft sensor methods for regression applications (2016, background): “can be thought as a good strategy when dealing with a large number of input variables [138], since it corresponds to search randomly over the input space, but following a”
Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis (2015, background): “Excellent review papers of feature selection methodologies are [17] or [31].”
Genetic programming for simultaneous feature selection and classifier design (2006, background): “In [11], it has been shown that GA based FS performs better than many conventional FS techniques for high-dimensional data.”
Investigating Speech Features for Continuous Turn-Taking Prediction Using LSTMs (2018, methodology): “In addition to testing different feature groupings, we run an experiment using the widely used sequential forward selection (SFS) algorithm [17] on the 21 acoustic features to investigate which, and how many, of these features are most useful to turntaking prediction.”
A survey of evolutionary algorithms for data mining and knowledge discovery (2003, background): “Comprehensive comparisons between GA and other attribute-selection algorithms, across a number of data sets, are reported in [69] and [42].”
Optimal number of features as a function of sample size for various classification rules (2005, methodology): “Evaluation of methods is generally comparative and based on simulations (Jain and Zongker, 1997; Kudo and Sklansky, 2000).”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, background): “A large variety of feature selection techniques that result in a suboptimal feature subset have been proposed (Kudo and Sklansky 2000; Verikas and Bacauskiene 2002).”
Unsupervised Feature Selection Using Feature Similarity (2002, methodology): “GA based feature selection methods [6] are usually found to perform better than other heuristic search methods for large and medium sized data sets; however, they also require considerable computation time for large data sets.”
Feature selection based on rough sets and particle swarm optimization (2007, background): “(Kudo and Sklansky, 2000).”
A hybrid genetic algorithm for feature selection wrapper based on mutual information (2007, background): “Kudo and Sklansky (2000) made a comparison among many of the feature selection algorithms and explicitly recommended that GAs should be used for large-scale problems with more than 50 candidate variables.”
Overview of Advanced Computer Vision Systems for Skin Lesions Characterization (2009, background): “detailed description of feature selection methodologies may be found in [72].”
Character Recognition Systems: A Guide for Students and Practitioners (2007, methodology): “Typical randomized search techniques include random mutation hill climbing [28, 87, 101], genetic algorithms [62, 96, 111, 112, 118, 120], simulated annealing [80], and tabu search [126].”
A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, methodology): “Back-tracking can be used in various algorithms like branch and bound, beam search, and best first search techniques [41].”