Particle swarm optimization-based feature selection in sentiment classification

Shang, Lin; Zhou, Zhe; Liu, Xing

doi:10.1007/s00500-016-2093-2

Cited by 84 publications

(40 citation statements)

References 25 publications

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“…The summary is shown in Figure 7. With the generation of large amounts of data and the incorporation of the Internet of things, there is a large space to use metaheuristics associated with combinatorial problems in the area of image processing and feature selection [125], deep learning tuning parameters [126], data intensive applications [127][128][129][130], and network and complex systems [131]. In this context, feature selection (FS) has been resolved using angle modulation and the set-based approach.…”

Section: Discussionmentioning

confidence: 99%

Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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“…It is a current vaiable (Ppbest(s)) and good positions (Pgbest(s)). PSO is locate the position and velocity as follows Vide (s+1) = WVi (s) + a1 * q1 * (P p best (s) Xide (s)) + g1 (1) g1= a2 * q2 * (P g best (s) -Xide (s))…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The farmers are dealing with seasonal variability in short term is very crucial. The relevance and quality agricultural dataset is vital for farmers who requires accurate predictions of crop yield to help make strategic decisions [1]. The combination of Principal Component Analysis (PCA) is for preprocessing and a modified Genetic Algorithm (GA) is used to get crop yield.…”

Section: Introductionmentioning

confidence: 99%

Classification Models and Hybrid Feature Selection Method to Improve Crop Performance

2019

IJITEE

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In this paper classification models and hybrid feature selection methods are implanted on benchmark dataset on the Mango and Maize. Particle Swarm Optimization–Support Vector Machine (PSO-SVM) classification algorithm for the selection of important features from the Mango and Maize datasets to analysis and also compare with the novel classification techniques. Various experiments conducted on these datasets, provide more generated rules and high selection of features using PSO-SVM algorithm and Fuzzy Decision Tree. The proposed method yield high accuracy output as compared to the existing methods with minimum Error Rate and Maximum Positive Rate.

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“…Moreover, Shang et al [30] conducted sentiment classification using a modified binary PSO (BPSO) model for feature selection. Known as F-BPSO, a mutation rate and a fitness proportionate selection strategy were employed for velocity updating and mitigating premature convergence of the original BPSO model.…”

Section: Pso-based and Other Feature Selection Methodsmentioning

confidence: 99%

Intelligent skin cancer detection using enhanced particle swarm optimization

Tan

Zhang

Neoh

et al. 2018

Knowledge-Based Systems

105

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In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature optimization. Since the identification of the most significant discriminative characteristics of the benign and malignant skin lesions plays an important role in robust skin cancer detection, the proposed PSO algorithm is employed for feature optimization. It incorporates not only subswarms, local and global food and enemy signals, attraction and flee operations, and mutation-based local exploitation, but also diverse matrix representations to mitigate premature convergence of the original PSO algorithm. Specifically, two remote swarm leaders, which show similar fitness but low position proximity, are used to lead the subswarm-based search and to enable the exploration of more distinctive search regions. Modified velocity updating strategies are also proposed to enable the particles to follow multiple swarm leaders and avoid the local and global worst individuals, partially (i.e. in randomly selected sub-dimensions) and fully (in every dimension), with an attempt to search for global optima. Probability distribution and dynamic matrix representations are used to diversify the search process. Evaluated with multiple skin lesion and UCI databases and diverse unimodal and multimodal benchmark functions, the proposed PSO variant shows a superior performance over those of other advanced and classical search methods for identifying discriminative features that facilitate benign and malignant lesion classification as well as for solving diverse optimization problems with different landscapes. The Wilcoxon rank sum test is adopted to further ascertain superiority of the proposed algorithm over other methods statistically.

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Particle swarm optimization-based feature selection in sentiment classification

Cited by 84 publications

References 25 publications

Putting Continuous Metaheuristics to Work in Binary Search Spaces

Putting Continuous Metaheuristics to Work in Binary Search Spaces

Classification Models and Hybrid Feature Selection Method to Improve Crop Performance

Intelligent skin cancer detection using enhanced particle swarm optimization

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