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hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization

Journal of Electronic Science and Technology 2019年第1期17卷 26-40页

作者： Jia Wu Xiu-Yun Chen Hao Zhang Li-Dong Xiong Hang Lei Si-Hao Deng the School of Information and Software Engineering University of Electronic Science and Technology of China Université de Technologie de Belfort-Montbéliard

hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techniques have been developed and successfully applied for certain application domains. However, this work demands professional knowledge and expert experience. And sometimes it has to resort to the brute-force ***, if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method, it will greatly improve the efficiency of machine learning. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes. In this way, the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem. Bayesian optimization is based on the Bayesian theorem. It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function. A utility function selects the next sample point to maximize the optimization *** experiments were conducted on standard test datasets. Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models, such as the random forest algorithm and the neural networks, even multi-grained cascade forest under the consideration of time cost.

关键词： Bayesian optimization Gaussian process hyperparameter optimization machine learning

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The posterior selection method for hyperparameters in regularized least squares method

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Control Theory and Technology 2024年第2期22卷 184-194页

作者： Yanxin Zhang Jing Chen Yawen Mao Quanmin Zhu School of Science Jiangnan UniversityWuxi214122JiangsuChina The Science and Technology on Near-Surface Detection Laboratory Wuxi214122JiangsuChina Department of Engineering Design and Mathematics University of the West of EnglandBristolBS161QYUK

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

关键词： Regularization method hyperparameter System identification Least squares algorithm

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Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 77-88页

作者： Shangpeng Zhong Xiaoming Wang Bin Xu Hongbin Wu Ming Ding the Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving Hefei University of TechnologyHefei 230009China the State Grid Anhui Electric Power Research Institute Hefei 230061China

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV ***,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training ***,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction ***,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction *** simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

关键词： New photovoltaic(PV)plant short-term predic tion time-series generative adversarial network(TimeGAN) hy brid network hyperparameter

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A hierarchical optimisation framework for pigmented lesion diagnosis

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CAAI Transactions on Intelligence Technology 2022年第1期7卷 34-45页

作者： Audrey Huong KimGaik Tay KokBeng Gan Xavier Ngu Department of Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia Department of Electrical Electronic&Systems EngineeringUniversiti Kebangsaan MalaysiaBangiMalaysia Institute of Integrated Engineering Universiti Tun Hussein Onn MalaysiaParit RajaMalaysia

The study of training hyperparameters optimisation problems remains underexplored in skin lesion *** is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the *** authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions *** the authors'demonstration,pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters,namely epoch,mini-batch value,initial learning rate,and gradient *** iterative search of the optimal global-local solution is by using the derivative-based *** authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training *** authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning *** authors'results showed that a small fraction of combinations(5%)from constrained global search space,in contrarily to 82%at the local level,can converge with early stopping *** mean(standard deviation,SD)validation accuracies increased from 78.4(4.44)%to 82.9(1.8)%using the authors'*** fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy,precision,sensitivity,and specificity of 85.3%,75.6%,64.4%,and 97.2%,*** authors'system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors'*** extended experiments also showed its superior performance consistency across different deep networks,where the overall classification accuracy increased by 5%with this *** approach reduces the risk of search being trapped in a suboptimal solution,and its use may be expanded to network architecture optimisation for

关键词： hierarchical hyperparameter optimisation pigmented lesion search

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Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

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Computer Systems Science & Engineering 2023年第10期47卷 221-238页

作者： G.Anitha N.Supriya Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Jinsang You Department of Electronics and Instrumentation Engineering B.S.Abdur Rahman Crescent Institute of Science and TechnologyChennai600048India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Seculayer Company Ltd.Seoul04784Korea

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers *** detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed *** this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)*** ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL *** fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of *** the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the *** illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are *** comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

关键词： Rooftop units chicken swarm optimization hyperparameter metaheuristics deep learning fault diagnosis

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Optimized Predictive Framework for Healthcare Through Deep Learning

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Computers, Materials & Continua 2021年第5期67卷 2463-2480页

作者： Yasir Shahzad Huma Javed Haleem Farman Jamil Ahmad Bilal Jan Abdelmohsen A.Nassani Department of Computer Science University of PeshawarPeshawar25120Pakistan Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science FATA UniversityKohat26100Pakistan Department of Management College of Business AdministrationKing Saud UniversityRiyadhSaudi Arabia

Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science *** data usually exist in unstructured,noisy,incomplete,and heterogeneous *** these limitations remains an open challenge in deep learning to classify health *** this paper,a long short-term memory(LSTM)based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health *** imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling *** proposed model is optimized and ne-tuned in an end to end manner to select ideal parameters using tree parzen estimator to build a probabilistic *** patient’s medication is pigeonholed to plot the diabetic condition’s risk factor through an algorithm to classify blood glucose metrics using a modern surveillance error grid *** proposed model can efciently train,validate,and test noisy data by obtaining consistent results around 90%over the state of the art machine and deep learning techniques and overcoming the insufciency in training data through transfer *** overall results of the proposed model are further tested with secondary datasets to verify model sustainability.

关键词： Recurrent neural network long short-term memory deep learning Bayesian optimization surveillance error grid hyperparameter

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On DFT Molecular Simulation for Non-Adaptive Kernel Approximation

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Advances in Materials Physics and Chemistry 2014年第6期4卷 105-115页

作者： Maharavo Randrianarivony Virtual Material Design Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin Germany

Using accurate quantum energy computations in nanotechnologic applications is usually very computationally intensive. That makes it difficult to apply in subsequent quantum simulation. In this paper, we present some preliminary results pertaining to stochastic methods for alleviating the numerical expense of quantum estimations. The initial information about the quantum energy originates from the Density Functional Theory. The determination of the parameters is performed by using methods stemming from machine learning. We survey the covariance method using marginal likelihood for the statistical simulation. More emphasis is put at the position of equilibrium where the total atomic energy attains its minimum. The originally intensive data can be reproduced efficiently without losing accuracy. A significant acceleration gain is perceived by using the proposed method.

关键词： DFT Energy Stochastic Covariance hyperparameter

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Erratum to “On DFT Molecular Simulation for Non-Adaptive Kernel Approximation” [Advances in Materials Physics and Chemistry Vol. 4 No. 6 (June 2014) 105-115]

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Advances in Materials Physics and Chemistry 2015年第3期5卷 95-95页

作者： Maharavo Randrianarivony Virtual Material Design Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin Germany

The original online version of this article (Randrianarivony, M. (June 2014) On DFT Molecular Simulation for Non-Adaptive Kernel Approximation. Advances in Materials Physics and Chemistry, Vol. 4 No. 6, 105-115. http://***/10.4236/ampc.2014.46013) did not contain any acknowledgment. The author wishes to add the following acknowledgements: Acknowledgements: This work was partially supported by Eurostars Project E!6935 funded by German Federal Ministry of Education and Research.

关键词： DFT Energy Stochastic Covariance hyperparameter

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