An improved particle swarm optimization for feature selection

By Li-Fei Chen, Chao-Ton Su, Kun-Huang Chen. Intelligent Data Analysis, 2011

350 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3af566431ee7215c32c99eaae22164c1403e7e65

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In Artificial Intelligence

Citations in the corpus

Machine Learning and Data Mining Methods in Diabetes Research (2017, methodology): “It combines electromagnetism-like mechanism (EM) algorithm with the nearest neighbor classi ﬁ er [37] and opposite sign test (OST) [38] as the local search.”
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).”
Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017, background): “The main goal of multi-objective optimization is to search for a set of non-dominated solutions.”
Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering (2017, background): “A population consists of S solutions, each solution represents the potential of solving the problem in a multi-dimensional search space [10,25].”
Moth–flame optimization algorithm: variants and applications (2019, background): “…Wang et al. [126] O ð HMS (cid:5) M þ HMS (cid:5) log ð HMS ÞÞ [124] Convergence Smoothconvergencewithfastrate [71] Slowconvergencerate[110] [127] [72] Suffer from premature convergence [49] [135] Strength Balancebetweenexplorationandexploitation [37] Balance between [87] Deal with the…”
A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “PSO with multiple swarms to share experience has also been applied to feature selection [11], [179], but may lead to the problem of high computational cost.”
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, positive): “[9] introduce a multiswarm PSO algorithm to search for the optimal feature subset and optimize the parameters of SVM simultaneously.”