Multivariate Approach for Alzheimer’s Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization

Zhang, Yudong; Wang, Shuihua; Sui, Yuxiu; Yang, Ming; Liu, Bin; Cheng, Hong; Sun, Jianfang; Jia, Wenhao; Phillips, P. Jonathon; Górriz, Juan Manuel

doi:10.3233/jad-170069

Cited by 140 publications

(54 citation statements)

References 57 publications

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“…Feature selection is a combinatorial optimization problem, which can be solved by Particle Swarm Optimization (PSO) [Zhang, Wang, Sui et al (2017)]. Xue et al studied on multi-objective particle swarm optimization (PSO) for feature selection, and it achieved comparable results with the existing well-known multi-objective algorithms in most cases [Xue, Zhang and Browne (2013)].…”

Section: Psomentioning

confidence: 99%

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Ma¹,

Zhou²,

Jia³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

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Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In this article, a feature selection algorithm with local search strategy based on the forest optimization algorithm, namely FSLSFOA, is proposed. The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest. Next, the fitness function is improved, which not only considers the classification accuracy, but also considers the size of the feature subset. To avoid falling into local optimum, a novel global seeding method is attempted, which selects trees on the bottom of candidate set and gives the algorithm more diversities. Finally, FSLSFOA is compared with four feature selection methods to verify its effectiveness. Most of the results are superior to these comparative methods.

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Section: Psomentioning

confidence: 99%

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Ma¹,

Zhou²,

Jia³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

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“…The adaptive inertia weight strategy and chaos map were also introduced called a predator-prey adaptive chaotic PSO (PAC-PSO) algorithm. The predator-prey PSO was applied to train the weights and biases of the NN (Neural Networks) classifier for alcoholism detection [27] and Alzheimer's disease detection [28].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Hybrid Swarm Intelligence Approach for Blog Success Prediction

Hsu¹,

Wu²,

Chiu³

2019

IJCIS

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A B S T R A C TSuccessful blogs receive high ratings and generate marketing value. What factors contribute to the success of a blog and how to predict its success level are questions worth discussing. A hybrid swam intelligence approach is proposed in this study to predict blog success level. First, this study develops a research model of blog success with six factors from content, technology, and social views of point, which include currentness, design, reliability, security, interaction, and connectivity. A questionnaire is designed based on the blog success model. Two hundred ten valid samples are collected from Internet users with experience in using or creating blogs. A hybrid approach combining particle swarm optimization (PSO) and self-organizing map (SOM) is proposed to predict blog success level. The results of 10-fold validation are examined to compare the hybrid PSO-SOM approach with the results from three classifiers: C5.0, classification and regression trees (CARTs), and support vector machine (SVM). For blog success prediction, the results indicate the PSO-SOM approach demonstrates higher accuracy among these methods.

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“…PSO can be easily implemented and is computationally inexpensive, and has few parameters to adjust. For its superiority, PSO has rapidly developed with applications in solving real-world optimization problems in recent years [2,3]. However, PSO is easily trapped into the local optima, and premature convergence appears when it is applied to complex multimodal problems [4].…”

Section: Introductionmentioning

confidence: 99%

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Chen¹

2019

Information

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Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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Multivariate Approach for Alzheimer’s Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization

Abstract: In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.

Cited by 140 publications

References 57 publications

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

A Hybrid Swarm Intelligence Approach for Blog Success Prediction

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

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