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13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)

作者： Batygin, Roman I. Alsova, Olga K. Novosibirsk State Tech Univ Comp Engn Novosibirsk Russia

ISBN: (纸本)9781509040698

This article describes the structure and functional content of a developed software system of different types of data classification based on the ensemble algorithms. Also the article describes the developed heterogeneous ensemble classification algorithm implemented in the software system. A distinctive feature of the algorithm is the iterative use of single (basic) classifiers on the initial training sample and inclusion in the ensemble only those classifiers whose relative error does not exceed a predetermined threshold. The software system was tested on real medical data. The accuracy of data classification using the basic classifiers and heterogeneous ensemble algorithm was compared. Test results showed the effectiveness (increase classification accuracy) of heterogeneous ensemble algorithm compared with the single classifiers.

关键词： single (basic) classification algorithm ensemble algorithm heterogeneous ensemble bagging boosting random forests bootstrap - sample decision tree logistic regression neural network

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Predicting harvesting efficiency of microalgae with magnetic nanoparticles using machine learning models

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第2期13卷

作者： Fu, Yu Zhang, Qingran Tan, Zhengying Yu, Songxia Zhang, Yi Guizhou Acad Sci Guizhou Acad Testing & Anal Guiyang 550014 Guizhou Peoples R China Tongji Univ Sch Environm Sci & Engn State Key Lab Pollut Control & Resources Reuse Shanghai 200092 Peoples R China

The emerging magnetic flocculation (or harvesting) with magnetic nanoparticles (MNPs) is a promising technology for microalgae dewatering. However, unearthing MNPs with high harvesting efficiency (HE) toward diverse microalgae relies on laborious experiments so far, a robust approach therefore is urgently needed to preevaluate the harvesting power of MNPs. Here, we predicted HE using machine learning algorithms across 1151 data points, in which the properties of MNPs and microalgae, and conditions of magnetic flocculation were comprehensively considered. Among 8 machine learning algorithms, the optimal XGBoost model showcased the best predictive performance with a high coefficient of determination (0.932), a low mean square error (6.96 %), and a low mean absolute error (4.17 %) on the test dataset. The model was also verified by batch experiments, demonstrating its ability to estimate HE accurately. Further, the Shapley additive explanations approach was used to decipher how the model made predictions from local and global perspectives, and these interpretations may offer guidelines for both the rational design of MNPS and the selection of microalgae species in magnetic flocculation. This work highlights the introduction of machine learning models to predict the harvesting ability of diverse MNPs toward microalgae, paving the way for the utilization of microalgal biomass.

关键词： Magnetic flocculation Magnetic nanoparticles Microalgae Machine learning Shapley additive explanation ensemble algorithm

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Imputation of missing data using ensemble algorithms

Imputation of missing data using ensemble algorithms

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Lu, Xiaoling Si, Jiesheng Pan, Lanfeng Zhao, Yanyun Center for Applied Statistics Renmin University of China Beijing China School of Statistics Renmin University of China Beijing China

ISBN: (纸本)9781612841816

Missing data or incomplete data are very common in statistical situations. One way to deal with missing data is to conduct model imputation either one time or multiple times. One of the key problems in analyzing the imputed dataset is to give the valid statistical reference of the parameter estimated, that is, to give a right estimation of the standard error of the interested statistic. This paper proposes the new developed ensemble algorithms as imputation model. In order to realize multiple imputation, we suggest bootstrap sampling the prediction error several times. The properties of the proposed methods are studied by simulation and compared with existing methods. Finally, the methods are applied to analyze one real large dataset, taking the missing mechanism into consideration. © 2011 IEEE.

关键词： ensemble algorithm imputation missing data

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An intrusion detection algorithm based on bag representation with ensemble support vector machine in cloud computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第24期32卷 e5922-e5922页

作者： Wei, Jinxia Long, Chun Li, Jiawei Zhao, Jing Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

The increase of security incidents brings a challenge to the cloud computing security. Intrusion detection technologies have been applied to protect information in cloud from being compromised, and complicated learning-based detection methods have been used to improve the performance of intrusion detection systems. Higher quality and well-formed samples are crucial to the performance of detection algorithm. Therefore, we mainly study the intrusion detection model based on data optimization processing. In this article, we establish an intrusion detection algorithm based on ensemble support vector machine with bag representation. Specifically, the sample flows are divided into bags, where the sample flows in each bag are related to each other. Each bag contains multiple related data flows that can accurately reflect intrusion behavior, especially persistent intrusion. What's more, ensemble algorithm is applied to detection model, which greatly optimizes the performance of detection algorithm. The experimental results on open access datasets show that the proposed model detects the persistent attack with 90.58% recall.

关键词： bag representation ensemble algorithm intrusion detection support vector machine

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STABILIZED SCALAR AUXILIARY VARIABLE ensemble algorithmS FOR PARAMETERIZED FLOW PROBLEMS

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2021年第4期43卷 A2869-A2896页

作者： Jiang, Nan Yang, Huanhuan Univ Florida Dept Math Gainesville FL 32611 USA Shantou Univ Dept Math Shantou 515063 Guangdong Peoples R China

Computing a flow system a number of times with different samples of flow parameters is a common practice in many uncertainty quantification applications, which can be prohibitively expensive for complex nonlinear flow problems. This report presents two second order, stabilized, scalar auxiliary variable (SAV) ensemble algorithms for fast computation of the Navier-Stokes flow ensembles: Stab-SAV-CN and Stab-SAV-BDF2. The proposed ensemble algorithms are based on the ensemble timestepping idea which makes use of a quantity called the ensemble mean to construct a common coefficient matrix for all realizations at the same time step after spatial discretization, in which case efficient block solvers, e.g., block GMRES, can be used to significantly reduce both storage and computational time. The adoption of a recently developed SAV approach that treats the nonlinear term explicitly results in a constant shared coefficient matrix among all realizations at different time steps, which further cuts down the computational cost, yielding an extremely efficient ensemble algorithm for simulating nonlinear flow ensembles with provable long time stability without any time step conditions. The SAV approach for the Navier-Stokes equations for a single realization was proved to be unconditionally stable in [L. Lin, Z. Yang, and S. Dong, J. Comput. Phys., 388 (2019), pp 1-22;X. Li and J. Shen, SIAM J. Numer. Anal., 58 (2020), pp. 2465-2491]. However we found the SAV approach has very low accuracy that compromises its stability in our initial numerical investigations for several commonly tested benchmark flow problems. In this report, we propose to use the stabilization -alpha h Delta(u(n+1)-u(n)) in Stab-SAV-CN and -alpha h Delta(3u(n+1)-4u(n)+u(n-1)) in Stab-SAV-BDF2 to address this issue. We prove that both of our ensemble algorithms are long time stable under one parameter fluctuation condition, without any time step constraints. For a single realization, both algorithms are uncondi

关键词： Navier-Stokes equations ensemble algorithm uncertainty quantification scalar auxiliary variable stabilization

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Second Order, Unconditionally Stable, Linear ensemble algorithms for the Magnetohydrodynamics Equations

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JOURNAL OF SCIENTIFIC COMPUTING 2023年第2期94卷 41-41页

作者： Carter, John Han, Daozhi Jiang, Nan Missouri Univ Sci & Technol Dept Math & Stat Rolla MO 65409 USA Univ Florida Dept Math Gainesville FL USA

We propose two unconditionally stable, linear ensemble algorithms with pre-computable shared coefficient matrices across different realizations for the magnetohydrodynamics equations. The viscous terms are treated by a standard perturbative discretization. The nonlinear terms are discretized fully explicitly within the framework of the generalized positive auxiliary variable approach (GPAV). Artificial viscosity stabilization that modifies the kinetic energy is introduced to improve accuracy of the GPAV ensemble methods. Numerical results are presented to demonstrate the accuracy and robustness of the ensemble algorithms.

关键词： MHD SAV Uncertainty quantification ensemble algorithm Unconditional stability

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Estimating Aboveground Biomass Using Sentinel-2 MSI Data and ensemble algorithms for Grassland in the Shengjin Lake Wetland, China

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REMOTE SENSING 2021年第8期13卷 1595-1595页

作者： Li, Chunhua Zhou, Lizhi Xu, Wenbin Anhui Univ Sch Resources & Environm Engn Hefei 230601 Peoples R China Anhui Univ Anhui Prov Key Lab Wetland Ecosyst Protect & Rest Hefei 230601 Peoples R China Management Bur Anhui Shengjin Lake Natl Nat Reser Chizhou 247210 Peoples R China

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g center dot m(-2) and R-2 of 0.844 for RF, RMSE of 112.425 g center dot m(-2) and R-2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g center dot m(-2), RMSE = 125.879 g center dot m(-2) in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 x 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which

关键词： aboveground biomass Sentinel-2 MSI ensemble algorithm red-edge GLCM Shengjin Lake wetland

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SAV decoupled ensemble algorithms for fast computation of Stokes-Darcy flow ensembles

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 387卷 114150-114150页

作者： Jiang, Nan Yang, Huanhuan Univ Florida Dept Math Gainesville FL 32611 USA Shantou Univ Dept Math Shantou 515063 Guangdong Peoples R China

Numerical modeling and simulation of complex systems is often subject to uncertainties in model parameters. Many popular uncertainty quantification (UQ) methods require repeated simulations of the underlying physical system with different samples of the uncertain model parameters. This poses great challenges to many practical engineering applications due to the high demand for computational resources. In this report we propose highly efficient ensemble simulation algorithms for fast computation of coupled flow ensembles. The proposed ensemble algorithms are based on two recently developed numerical approaches: scalar auxiliary variable (SAV) and ensemble timestepping. We introduce a new decoupling strategy using the SAV idea and incorporate the ensemble timestepping method to develop two decoupled ensemble schemes for the Stokes-Darcy system: SAY-BE-En and SAV-BDF2-En. The two ensemble algorithms are specially designed for UQ computations where a number of realizations of the underlying coupled PDE system are required for analyzing and interpreting flow statistics. Compared with traditional methods which solve for each realization independently, our proposed ensemble algorithms result in a common coefficient matrix for all realizations and efficient iterative solvers such as block CG or block GMRES can be used to solve for all realizations simultaneously reducing both computer storage and overall simulation time. We prove that both ensemble algorithms are long time stable without any time step conditions. We also provide a comprehensive error analysis for the fully discrete SAY-BE-En algorithm, and present a few illustrative numerical examples to demonstrate the efficiency and effectiveness of the algorithms. (C) 2021 Elsevier B. V. All rights reserved.

关键词： Stokes-Darcy equations ensemble algorithm Uncertainty quantification Scalar auxiliary variable

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An ensemble Load Balancing algorithm to Process the Multiple Transactions Over Banking

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第8期14卷 591-596页

作者： Dornala, Raghunadha Reddi Cloud Architect Annandale VA 22003 USA

The banking industry has been transformed by cloud computing, which has provided scalable and cost-effective solutions for managing large volumes of transactions. However, as the number of transactions grow, the need for efficient load-balancing algorithms to ensure optimal utilization of cloud resources and improve system performance becomes critical. This paper proposes an ensemble cloud load-balancing (ECBA) algorithm specifically designed to process multiple banking transactions. The proposed algorithm combines the strengths of several load-balancing techniques to achieve a balanced distribution of transaction loads in various cloud servers. It considers factors such as transaction types, server capacities, and network conditions to make intelligent load distribution decisions. The algorithm dynamically adapts to changing workload patterns and optimizes resource allocation by leveraging machine learning and predictive analytics. A simulation environment that mimics the banking system's transaction processing workflow is created to evaluate the performance of the ensemble load balancing algorithm. Extensive experiments with various workload scenarios are conducted to assess the algorithm's effectiveness in load balancing, response time, resource utilization, and overall system performance. The results show that the proposed ECBA outperforms traditional banking load-balancing approaches. It reduces response time, improves resource utilization, and ensures every server is adequately funded with a few transactions. The algorithm's adaptability and scalability make it well-suited for handling dynamic and fluctuating workloads, thus providing a robust solution for processing multiple transactions in the banking sector.

关键词： Cloud computing load balancing ensemble algorithm banking transaction processing resource utilization response time scalability

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An ensemble SVR Based on Modified Adaboost - RT algorithm for Predicting the Degradation of a Gas Turbine Compressor 7th

An Ensemble SVR Based on Modified Adaboost - RT Algorithm fo...

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Prognostics and System Health Management Conference

作者： Zhicai Zhou Dongfeng Liu Qingdao Oil Detection and Analysis Center Naval University of Engineering Qingdao China

ISBN: (纸本)9781509027798

The original AdaBoost. RT algorithm determines the fixed weights of combined machines by learning the errors of each training sample, but the difference in learning ability between the new sample and the training samples is neglected. To address this problem, this study propose an ensemble support vector regression machine (SVR) based on modified *** algorithm. Firstly, an SVR algorithm is selected as the ensemble predictor due to its strong performance and robustness. Secondly, a modified *** combined k-nearest neighbor algorithm (kNN) is proposed which overcomes the limitation of the original *** by dynamically adjusting the weights of the combined machines. Finally, an ensemble SVR based modified *** is presented and examined by predicting the gas turbine compressor degradation, and compared with models that are established using a single SVR and original ***. The experiments have demonstrated that the proposed method can improve the general performance and effectively boost the accuracy, and the method is satisfactory to predict the process of compressor degradation in real time.

关键词： AdaBoost.RT Support vector regression machine (SVR) ensemble algorithm Compressor Gas turbine

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