2016
DOI: 10.3906/elk-1404-220
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A hybrid search method of wrapper feature selection by chaos particle swarm optimization and local search

Abstract: Finding a subset of features from a large dataset is a problem that arises in many fields of study. Since the increasing number of features has extended the computational cost of a system, it is necessary to design and implement a system with the least number of features. The purpose of feature selection is to find the best subset of features from the original ones. The result of the best selection is improving the computational cost and the accuracy of the prediction.A large number of algorithms have been pro… Show more

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Cited by 12 publications
(5 citation statements)
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“…Ultimately, to avoid being stuck into local minima, PSOMA used the simulated annealing with multiple neighborhood search strategies. It is worth mentioning that the local search has been used with the PSO for tackling several optimization problems, and this confirms that the local search has a significant influence on the performance after integration; some of those works are comprehensive learning PSO with a local search for multimodal functions [23], PSO with local search [24], and many others [2,[25][26][27][28][29].…”
Section: Of 24mentioning
confidence: 99%
“…Ultimately, to avoid being stuck into local minima, PSOMA used the simulated annealing with multiple neighborhood search strategies. It is worth mentioning that the local search has been used with the PSO for tackling several optimization problems, and this confirms that the local search has a significant influence on the performance after integration; some of those works are comprehensive learning PSO with a local search for multimodal functions [23], PSO with local search [24], and many others [2,[25][26][27][28][29].…”
Section: Of 24mentioning
confidence: 99%
“…This purpose is achieved by introducing new approaches to modify the algorithm or to hybridize two algorithms. Hybrid big bang big crunch with local search (Genc et al , 2013), hierarchical approximate Bayesian computation–migrating birds (Makas and Yumuşak, 2016), chaotic PSO (Javidi and Emami, 2016) and modified GA (Dörterler et al , 2017) are some of the approaches used by researchers to improve the proficiency of algorithms. To improve the balance, some researchers have introduced scads of modifications to algorithms implemented successfully in power systems (Nayak et al , 2018a, 2018b).…”
Section: Introductionmentioning
confidence: 99%
“…So, it needs to take measures to strengthen PSO's local search. In [10][11][12], local search strategies were introduced to find the optimal value in which the search direction of particles was controlled and aimed to balance the diversity of the population and the convergence speed of the particle swarm. Similarly a neighborhood-based mutation operator [13] is introduced to enhance the population diversity.…”
Section: Introductionmentioning
confidence: 99%