A discrete particle swarm optimization method for feature selection in binary classification problems

By Alper Ünler, Alper Ekrem Murat. European Journal of Operational Research, 2010

444 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5ed3aa8acc8953462da6eab46adbcdad1baef1b2

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In Artificial Intelligence

1. Yaochu Jin
2. Ran Cheng
3. Mengjie Zhang
4. Bing Xue
5. Shenkai Gu
6. Xing-yi Zhang
7. Ye Tian
8. Xin Yao

Citations in the corpus

• Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017, methodology): “The filter approach relies primarily on the general characteristics of a dataset to evaluate and select feature subsets without considering a special learning approach.”
• Particle Swarm Optimization: Technique, System and Challenges (2011, unknown): “PSO [61] [62] [63] [64]”
• Feature selection via a novel chaotic crow search algorithm (2017, methodology): “Some of these algorithms are gradient descent algorithm [10], discrete particle swarm optimization (DPSO) [46], tabu search [56], elephant herding optimization (EHO) [11], firefly algorithm (FA) [12], harmony search (HS) [14], charged system search (CSS) [25], bird swarm algorithm (BSA) [32], animal”
• Feature subset selection for logistic regression via mixed integer optimization (2016, methodology): “, [38]), for instance, tabu search [33] and particle swarm optimization [36] can be used for feature subset selection.”
• A Survey on Evolutionary Computation Approaches to Feature Selection (2016, background): “ary algorithms, PSO [10], [29], [42], and ACO [43]–[46] as typical examples in swarm intelligence, and other algorithms recently applied to feature selection, including differential evolution (DE) [47], [48],1 memetic algorithms [49], [50], LCSs [51], [52], evolutionary strategy (ES) [53], artificia”
• A comprehensive survey on recent metaheuristics for feature selection (2022, background): “, a modified version of PSO, for the binary feature selection problem [88].”
• Improved salp swarm algorithm based on particle swarm optimization for feature selection (2018, methodology): “However, most existing FS methods as the previously mentioned still suffer from trapping in local optima and high computational cost (Liu et al. 2011; Unler and Murat 2010).”
• Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “Recently, some researchers have proposed to apply PSO to feature selection [36,13].”
• 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…”