Improved binary PSO for feature selection using gene expression data

By Li-Yeh Chuang, Hsueh-Wei Chang, Chung-Jui Tu, Cheng-Hong Yang. Comput. Biol. Chem., 2008

566 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3542c3cf205f5b1e8fc7cb002104ac6143a2039a

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Citations in the corpus

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “2008) [128] Improved binary particle swarm”
Feature selection in machine learning: A new perspective (2018, methodology): “An improved binary particle swarm optimization is used to implement feature selection for gene selection, where the kNN accuracy is taken as the evaluator [106].”
An improved particle swarm optimization for feature selection (2011, methodology): “PSO is a stochastic optimization technique introduced recently by Kennedy and Eberhart who were inspired by the social behavior of bird flocking and fish schooling [17].”
Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014, methodology): “Both of them have been applied in feature selection problems and achieve good results [26, 4, 31].”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “…of metaheuristic algorithms on the considered datasets, we considered ﬁve metaheuristic algorithms i.e. binary particle swarm optimization (BPSO) [270], binary differential evolution (BDE) [271], binary ant lion optimizer (BALO) [183], binary grey wolf optimizer (bGWO) [170] and binary gaining…”
Binary grey wolf optimization approaches for feature selection (2016, methodology): “In this proposed system, the KNN is used as a classification to ensure the goodness of the selected features.”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, methodology): “have developed an improved binary PSO algorithm for feature selection using gene expression data [12].”
A survey on feature selection methods (2014, methodology): “A binary PSO [32,48,4] algorithm can also be used for wrapper implementation.”
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, unknown): “Although both versions of PSO have been successfully applied to single objective feature selection [3], [9], [10], [12], [13], binary PSO has potential limitations, such as the position of a particle in binary PSO is updated solely based on the velocity while the position in standard PSO is updated…”
Binary ant lion approaches for feature selection (2016, methodology): “Training set is used to evaluate the KNN on the validation set throughout the optimization to guide the feature selection process, while test data are kept hidden from the optimization and is used for final evaluation.”
A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “A number of EC algorithms have been proposed to solve large-scale feature selection problems [15], [70], [72], [118], [140], [175], [201], where the dimensionality reaches a”