A GPSO-optimized convolutional neural networks for EEG-based emotion recognition

Gao, Zhong-Ke; Li, Yanli; Yang, Yuxuan; Wang, Xinmin; Dong, Na; Chiang, Hsiao‐Dong

doi:10.1016/j.neucom.2019.10.096

Cited by 65 publications

(44 citation statements)

References 36 publications

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“…Gao et al [44] proposed a gradient-priority PSO algorithm for deep network generation for undertaking EEG-based emotion recognition. It addresses the efficiency limitations of automated architecture search in a high-dimensional search space.…”

Section: T+1mentioning

confidence: 99%

Particle Swarm Optimization for Automatically Evolving Convolutional Neural Networks for Image Classification

et al. 2021

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Designing Convolutional Neural Networks from scratch is a time-consuming process that requires specialist expertise. While automated architecture generation algorithms have been proposed, the underlying search strategies generally are computationally expensive. The existing methods also do not explore the search space efficiently, and often lead to sub-optimal solutions. In this research, we propose a novel Particle Swarm Optimization (PSO)-based model for deep architecture generation to address the above challenges. Our proposed solution incorporates three new components. Firstly, a group-based encoding strategy is devised, which enforces the candidate networks to always follow the best practices. Specifically, it ensures that the number of groups can be adjusted in accordance with the input image size. By restricting the number of groups, we can adapt the frequency of the pooling operations toward the input image size. As such, it ascertains the position and maximum frequency of the pooling operations always result in a valid network architecture without the need for additional complex governing rules. Secondly, a new velocity updating mechanism is devised, which creates new network architectures by identifying the key network configuration differences. Thirdly, a new position updating mechanism using weighted velocity strengths is devised. Both the velocity and position updating mechanisms facilitate the proposed PSO-based model to search the intermediate positions of the particles' trajectories, allowing a better trade-off between diversification and intensification to be achieved. We employ eight well-known data sets, including Convex, Rectangles, MNIST and its variants, for model evaluation. The proposed PSO-based model achieves up to 7.58% improvement in accuracy and up to 63% reduction in computational cost, in comparison with those from the current state-of-the-art methods.

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Section: T+1mentioning

confidence: 99%

Particle Swarm Optimization for Automatically Evolving Convolutional Neural Networks for Image Classification

et al. 2021

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“…Another interesting method was presented in [20], where the authors proposed the gradient-priority PSO (GPSO) with gradient penalties for selecting the CNN structure. The proposed method was tested by using three types of emotion states for each subject and simultaneously collecting EEG signals corresponding to each emotion category.…”

Section: Metaheuristics Applications For Cnn Optimizationmentioning

confidence: 99%

“…More details regarding the CNNs layers, evolution, and functional principles can be retrieved from [20,76].…”

Section: Mathematical Modelmentioning

confidence: 99%

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Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

et al. 2020

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Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.

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“…Therefore, an urgent issue in the deep learning domain is optimization of the model construction. According to recent studies [24,25], the model construction often affects its overall performance and requires to be automatically set. These studies revealed that optimizing the hyperparameters remains a major obstacle in designing the deep learning models, including CNNs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

et al. 2021

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Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

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A GPSO-optimized convolutional neural networks for EEG-based emotion recognition

Cited by 65 publications

References 36 publications

Particle Swarm Optimization for Automatically Evolving Convolutional Neural Networks for Image Classification

Particle Swarm Optimization for Automatically Evolving Convolutional Neural Networks for Image Classification

Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

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