Belief-based chaotic algorithm for support vector data description

Hamidzadeh, Javad; Namaei, Neda

doi:10.1007/s00500-018-3083-3

Cited by 17 publications

(7 citation statements)

References 41 publications

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“…These learning models have recently received a lot of attention in the machine learning community. For example, Hamidzadeh and Namaei (2019) proposed a chaosbased belief technology for describing support vector data. Hamidzadeh et al, (2017) suggested a chaotic bat algorithm for the weighted support vector data description.…”

Section: Highlightsmentioning

confidence: 99%

A hybrid feed-forward neural network with grasshopper optimization for observing pattern of scour depth around bridge piers

et al. 2021

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This paper uses the recently proposed grasshopper optimization algorithm (GOA) to develop a new hybrid, stochastic training algorithm for the feed-forward neural networks (FFNN). The state-of-the-art hybrid model is then applied to study observing pattern of scour depth which is a challenging problem in hydraulic engineering. In order to verify and control the accuracy, stability, and efficiency of the proposed model and its computational process, the model results are compared to networks trained by three different training algorithms. To achieve this, backpropagation, backpropagation with momentum, and Levenberg-Marquardt learning algorithms, which are widely used for various hydraulic problems, are chosen. The results of the model indicated that the proposed model has high stability and performance in solving regression problems. The comparison of prediction accuracy and convergence curves represented that the model could predict the maximum scour depth with the higher convergence speed. Applying the proposed model improves the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) values by approximately 6%, 30%, and 18%, respectively.

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Section: Highlightsmentioning

confidence: 99%

A hybrid feed-forward neural network with grasshopper optimization for observing pattern of scour depth around bridge piers

et al. 2021

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“…For example in [48], an extension of SVDD is proposed to handle non-stationary or increasing data. Recently, in [49], an algorithm developed for reducing the effect of uncertain data around the hypersphere of SVDD achieved state of the art result on many UCI [50] datasets. In this paper, we consider baseline SVDD combined with multimodal subspace learning.…”

Section: One-class Classificationmentioning

confidence: 99%

Subspace Support Vector Data Description

Sohrab

Raitoharju

Gabbouj

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. The data description in the new subspace is obtained by Support Vector Data Description. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulation. We conduct experiments on two multimodal datasets and compare the proposed approach with baseline and recently proposed one-class classification methods combined with early fusion and also considering each modality separately. We show that the proposed Multimodal Subspace Support Vector Data Description outperforms all the methods using data from a single modality and performs better or equally well than the methods fusing data from all modalities.

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“…In this work, the deep-level features and the radius of the hypersphere were jointly trained to enhance the performance of the model. To further improve the performance of the SVDD-based abnormal data detection model, Hamidzadeh et al [31,32] analyzed the influence of parameters such as kernel function and penalty factor on the performance of the SVDD model; then, an optimal SVDD model was established based on the chaotic bat optimization algorithm. Usually, this type of method regards empirical error as the optimization objective.…”

Section: Introductionmentioning

confidence: 99%

Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

Qiu

Song

Wang

2022

Meas. Sci. Technol.

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Abnormal data detection for industrial processes is essential in industrial process monitoring and is an important technology to ensure production safety. However, for most industrial processes, it is a challenge to establish an effective abnormal data detection model due to the following issues: (1) weak model performance due to the small amount of process data; (2) trade-offs between model sparsity and accuracy; and (3) weak generalization ability of abnormal data detection model. To address these issues, a method based on adversarial autoencoders support vector data description (AAESVDD) is presented in this work. First, a novel construction strategy is designed for a hybrid feature dataset based on the adversarial autoencoder (AAE). The hybrid feature dataset utilizes the latent feature and reconstruction residual extracted by the AAE to enhance the feature diversity of the process data. Then, combining the support vector data description (SVDD) and Bayesian optimization algorithm (BOA), an automatic detection model for abnormal data of the hybrid feature dataset is established. Meanwhile, a BOA objective function based on the criterion of the hybrid risk minimization (HRM) is proposed to automatically optimize the model parameters, which further enhances the generalization ability of the SVDD-based model. Finally, the effectiveness of the proposed AAESVDD method is illustrated with the UCI benchmark datasets and an industrial penicillin fermentation process.

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Belief-based chaotic algorithm for support vector data description

Cited by 17 publications

References 41 publications

A hybrid feed-forward neural network with grasshopper optimization for observing pattern of scour depth around bridge piers

A hybrid feed-forward neural network with grasshopper optimization for observing pattern of scour depth around bridge piers

Subspace Support Vector Data Description

Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

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