检索结果-内蒙古大学图书馆

Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

引用

Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 3221-3232页

作者： Deliang Sun Jing Wang Haijia Wen YueKai Ding Changlin Mi Key Laboratory of GIS Application Research Chongqing Normal UniversityChongqing 401331China Key Laboratory of New Technology for Construction of Cities in Mountain Area Ministry of EducationChongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China School of Civil Engineering Chongqing UniversityChongqing 400045China Natural Resources Development Service Center of Linyi Linyi 276000China

boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)***,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM *** investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average *** XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM ***,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM *** boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer ***,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer *** HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test *** model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study *** study offers a scientific reference for LSM and disaster prevention *** study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou *** proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM ***,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.

关键词： Landslide susceptibility Hyperparameter optimization boosting algorithms SHapley additive exPlanations(SHAP)

来源：评论

学校读者我要写书评

暂无评论

Vibration analysis of embedded porous nanobeams under thermal effects using boosting machine learning algorithms and semi-analytical approach

引用

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第29期31卷 12320-12343页

作者： Tariq, Aiman Uzun, Busra Deliktas, Babur Yayli, Mustafa Ozgur Bursa Uludag Univ Engn Fac Dept Civil Engn Bursa Turkiye

This study presents a thermal vibration analysis of functionally graded porous nanobeams using boosting machine learning models and a semi-analytical approach. Nonlocal strain gradient theory is employed to explore vibration behavior, accounting for thermal and size effects. A semi-analytical approach solution utilizing Fourier series and Stokes' transform to establish an eigenvalue problem capable of examining vibrational frequencies of porous nanobeams in both rigid and deformable boundary conditions is presented. Four boosting models including gradient boosting (GBoost), light gradient boosting (LGBoost), extreme gradient boosting (LGBoost), and adaptive boosting (AdaBoost) are employed to study the impact of seven crucial parameters on natural frequencies of a nanobeam. Sobol quasi-random space-filling method is used to generate the samples by varying input feature combinations for different porous nanobeam distributions. The model performance is assessed using statistical metrics, visualization tools, 5-fold cross-validation, and SHAP analysis for feature importance. The results highlight the effectiveness of boosting ML models in predicting natural frequencies, particularly XGBoost, which achieved an exceptional R2 value of 0.999, accompanied by the lowest MAE, MAPE, and RMSE values among the models assessed. LGBoost and AdaBoost follow XGBoost in performance, while GBoost exhibits relatively lower effectiveness, as highlighted by radar plots. The SHAP analysis revealed the significant impact of the foundation parameter on frequency prediction, with porosity coefficients notably influencing higher vibration modes.

关键词： Porous nanobeams vibration analysis boosting algorithms size effect SHAP analysis

来源：评论

学校读者我要写书评

暂无评论

boosting machine learning algorithms for predicting the macroscopic material behavior of continuous fiber reinforced composite

引用

JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2024年

作者： Tariq, Aiman Polat, Ayse Deliktas, Babur Bursa Uludag Univ Engn Fac Dept Civil Engn Gorukle CampusNilufer TR-16059 Bursa Turkiye

Macroscopic mechanical properties of fibrous materials are often characterized by modeling their microscale behavior using micromechanical techniques. This process typically involves using a Representative Volume Element (RVE) and finite element simulations to obtain the macroscopic behavior through homogenization. However, these micromechanical simulations can be computationally demanding, especially for 3D models with discrete material microstructures. This paper uses boosting machine learning algorithms to predict the homogenized macroscopic material behavior of heterogeneous composites. These models are trained on the micromechanical simulation results generated by varying the constitutive parameters of local phases and microscopic parameters such as fiber volume fraction. The Bayesian optimization is used to determine the best hyperparameters of the considered boosting models, which include adaptive boosting (AdaB), gradient boosting (GBR), light gradient boosting (LGB), and extreme gradient boosting (XGB). The performances of trained models are assessed using various metrics such as R2, MAE, MAPE, and RMSE and using various plots such as scatter plots, Taylor plots, radar plots, and bar plots. The comparative assessment showed that all the models predicted the homogenized stiffness matrix of the RVE successfully, with R2 values between 0.94 and 0.99. The XGB model presented the best overall performance. This work contributes to the field of composites by presenting a new and computationally efficient approach to predict the macroscopic behavior of RVEs using boosting models.

关键词： boosting algorithms fiber reinforced composite homogenization representative volume elements

来源：评论

学校读者我要写书评

暂无评论

Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs

引用

MARINE AND PETROLEUM GEOLOGY 2022年 145卷

作者： Al-Mudhafar, Watheq J. Abbas, Mohammed A. Wood, David A. Basrah Oil Co Basrah Iraq Basrah Oil Co Petrophys Basrah Iraq DWA Energy Ltd Lincoln England

Lithofacies classification from well logs recorded through heterogeneous carbonate reservoirs helps to improve reservoir discrimination with respect to fluid flow and storage capabilities. A novel technique is developed to classify carbonate lithofacies with the assistance of boosting algorithms for a well drilled into the large Majnoon oil and gas field of southern Iraq. Five boosting machine learning algorithms: Logistic boosting Regression (LogitBoost), Generalized boosting Modeling (GBM), Extreme Gradient boosting (XGBoost), Adaptive boosting Model (AdaBoost), and K-nearest neighbor (KNN) were comparatively implemented to attain the most realistic lithofacies predictions. Data from available cores provide actual lithofacies determination. Additionally, seven standard well logs recorded provide continuous additional information across the complete reservoir section. Random sub-sampling of the well-log dataset was used to train and test the models. Confusion matrices of the results provided Total Percent Correct (TPC) accuracy measures for LogitBoost, GBM, XGBoost, AdaBoost, and KNN with respect to the entire dataset of 97.65%, 96.99%, 97.74%, 97.74%, and 96.74%, respectively, and with respect to the validation subsets TPC was 94%, 96%, 98%, 91%, and 96%, respectively. The XGBoost provided more accurate lithofacies classification than the other five boosting algorithms with respect to the predictions generated for both the entire dataset and testing subset. The lithofacies predictions were then compared with poro-perm interpretations from the Nuclear Magnetic Resonance (NMR) log and oil rate from the Production Logging Tool (PLT). Rudist-rich and argillaceous lithofacies display lower porosity and permeability, respec-tively. The Rudist-rich lithotype is associated with high-production-rate zones, whereas the argillaceous facies is associated with low-production-rate zones. The boosting machine learning workflow developed efficiently provides accurate lithofacies

关键词： Reservoir characterization Lithofacies classification boosting algorithms Machine learning Carbonate reservoirs Well log data analysis

来源：评论

学校读者我要写书评

暂无评论

Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques

引用

COMPUTERS & STRUCTURES 2024年 305卷

作者： Zareian, Farzaneh Banazadeh, Mehdi Zareian, Mohammad Sajjad Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran Shahab Danesh Univ Dept Civil Engn Qom Iran

The main objective of this paper is to develop machine learning (ML) models for predicting the seismic responses of steel moment frames. For this purpose, four boosting ML techniques-gradient boosting, XGBoost, LightGBM, and CatBoost-were developed in Python. To create an inclusive dataset, 92,400 nonlinear time-history analyses were performed on 1,848 steel moment frames under 50 earthquakes using OpenSeesPy. Geometric configurations, structural properties, and ground motion intensity measures were considered as the inputs for the models. The outputs included maximum global drift ratio (MGDR), maximum interstory drift ratio (MIDR), base shear coefficient (BSC), and maximum floor acceleration (MFA). The study also investigated the effectiveness of the ML models in estimating fragility curves for an 8-story steel frame at different performance levels. Finally, a web application was developed to facilitate the estimation of the peak dynamic responses for steel moment frames. The results show that the LightGBM and CatBoost models demonstrate superior predictive performance, with coefficient of determinations (R2) higher than 0.925. Furthermore, the LightGBM models can estimate the fragility curves with minimal errors (e.g., the relative errors in the median values of the predicted curves are less than 10%).

关键词： Machine learning Nonlinear dynamic responses Fragility curves Steel moment frames boosting algorithms

来源：评论

学校读者我要写书评

暂无评论

BoosTexter: A boosting-based system for text categorization

引用

MACHINE LEARNING 2000年第2-3期39卷 135-168页

作者： Schapire, RE Singer, Y AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.

关键词： text and speech categorization multiclass classification problems boosting algorithms

来源：评论

学校读者我要写书评

暂无评论

boosting the visibility of services in microservice architecture

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3099-3111页

作者： Tokmak, Ahmet Vedat Akbulut, Akhan Catal, Cagatay Istanbul Kultur Univ Dept Comp Engn TR-34536 Istanbul Turkiye Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar

Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.

关键词： Service discovery Microservice architecture boosting algorithms CatBoost LightGBM XGBoost Gradient increase

来源：评论

学校读者我要写书评

暂无评论

Evidence contrary to the statistical view of boosting

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2008年第2期9卷 131-156页

作者： Mease, David Wyner, Abraham San Jose State Univ Coll Business Dept Marketing & Decis Sci San Jose CA 95192 USA Univ Penn Wharton Sch Dept Stat Philadelphia PA 19104 USA

The statistical perspective on boosting algorithms focuses on optimization, drawing parallels with maximum likelihood estimation for logistic regression. In this paper we present empirical evidence that raises questions about this view. Although the statistical perspective provides a theoretical framework within which it is possible to derive theorems and create new algorithms in general contexts, we show that there remain many unanswered important questions. Furthermore, we provide examples that reveal crucial flaws in the many practical suggestions and new methods that are derived from the statistical view. We perform carefully designed experiments using simple simulation models to illustrate some of these flaws and their practical consequences.

关键词： boosting algorithms LogitBoost AdaBoost

来源：评论

学校读者我要写书评

暂无评论

boosting methods for multi-class imbalanced data classification: an experimental review

引用

JOURNAL OF BIG DATA 2020年第1期7卷 1-47页

作者： Tanha, Jafar Abdi, Yousef Samadi, Negin Razzaghi, Nazila Asadpour, Mohammad Univ Tabriz Fac Elect & Comp Engn POB 51666-16471 Tabriz Iran

Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets. For this reason, researchers have been paid attention and have proposed many methods to deal with this problem, which can be broadly categorized into data level and algorithm level. Besides, multi-class imbalanced learning is much harder than binary one and is still an open problem. boosting algorithms are a class of ensemble learning methods in machine learning that improves the performance of separate base learners by combining them into a composite whole. This paper's aim is to review the most significant published boosting techniques on multi-class imbalanced datasets. A thorough empirical comparison is conducted to analyze the performance of binary and multi-class boosting algorithms on various multi-class imbalanced datasets. In addition, based on the obtained results for performance evaluation metrics and a recently proposed criteria for comparing metrics, the selected metrics are compared to determine a suitable performance metric for multi-class imbalanced datasets. The experimental studies show that the CatBoost and LogitBoost algorithms are superior to other boosting algorithms on multi-class imbalanced conventional and big datasets, respectively. Furthermore, the MMCC is a better evaluation metric than the MAUC and G-mean in multi-class imbalanced data domains.

关键词： boosting algorithms Imbalanced data Multi-class classification Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

An iterative boosting-based ensemble for streaming data classification

引用

INFORMATION FUSION 2019年 45卷 66-78页

作者： Bertini Junior, Joao Roberto Nicoletti, Maria do Carmo Univ Estadual Campinas Sch Technol Rua Paschoal Marmo 1888 Limeira Brazil FACCAMP Campo Limpo Paulista SP Brazil Univ Fed Sao Carlos Comp Sci Dept Sao Carlos SP Brazil

Among the many issues related to data stream applications, those involved in predictive tasks such as classification and regression, play a significant role in Machine Learning (ML). The so-called ensemble-based approaches have characteristics that can be appealing to data stream applications, such as easy updating and high flexibility. In spite of that, some of the current approaches consider unsuitable ways of updating the ensemble along with the continuous stream processing, such as growing it indefinitely or deleting all its base learners when trying to overcome a concept drift. Such inadequate actions interfere with two inherent characteristics of data streams namely, its possible infinite length and its need for prompt responses. In this paper, a new ensemblebased algorithm, suitable for classification tasks, is proposed. It relies on applying boosting to new batches of data aiming at maintaining the ensemble by adding a certain number of base learners, which is established as a function of the current ensemble accuracy rate. The updating mechanism enhances the model flexibility, allowing the ensemble to gather knowledge fast to quickly overcome high error rates, due to concept drift, while maintaining satisfactory results by slowing down the updating rate in stable concepts. Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification.

关键词： Ensemble learning Data stream classification boosting algorithms Incremental learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：