检索结果-内蒙古大学图书馆

3rd Information Systems International Conference

作者： Panthong, Rattanawadee Srivihok, Anongnart Kasetsart Univ Dept Comp Sci Bangkok 10900 Thailand

Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on the use of wrapper feature selection. This study use methods of sequential forward selection (SFS), sequential backward selection (SBS) and optimize selection (evolutionary) based on ensemble algorithms namely Bagging and AdaBoost by subset evaluations which are performed using two classifiers;Decision Tree and Naive Bayes. Thirteen datasets containing different numbers of attributes and dimensions are obtained from the UCI Machine learning Repository. This study shows that the search technique using SFS based on the bagging algorithm using Decision Tree obtained better results in average accuracy (89.60%) than other methods. The benefits of the feature subset selection are an increased accuracy rate and a reduced run-time when searching multimedia data consisting of a large number of multidimensional datasets. (C) 2015 Published by Elsevier B.V.

关键词： Feature selection ensemble learning algorithm dimension reduction classification data mining

来源：评论

学校读者我要写书评

暂无评论

ensemble learning with support vector machines algorithm for surface roughness prediction in longitudinal vibratory ultrasound-assisted grinding

引用

PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2024年 88卷 382-400页

作者： Zhao, Mingli Xue, Boxi Li, Bohan Zhu, Junming Song, Wenbin Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454000 Peoples R China

It is critical to have an accurate prediction of surface roughness (Sa) in order to improve grinding productivity, reduce costs, and minimize the period of time required for trials and testing. Although many prediction methods have been developed, fewer studies have been conducted on the prediction of surface roughness in longitudinal ultrasonic vibration-assisted grinding (LUVAG). In this paper, a surface roughness prediction model algorithm based on ensemble learning of support vector machines (ELSVM) is proposed that can be used for surface roughness prediction of LUVAG alumina ceramics. This paper first details the development of the ELSVM surface roughness prediction model, which consists of four modules: the prepossessing module, the multi-algorithm regression module, the support vector machine algorithm (SVM) module, and the ensemble module. In addition, ELSVM was compared with four other machine learning methods based on experimental results for surface roughness prediction modeling. The error of ELSVM model was reduced by 6.3%, 7.9%, 8.9%, and 7.5%, respectively, compared to the individual prediction models such as I-AISPSO, I-AIS, SPSO, and KBaNN. Comparison results show that the ELSVM model has the lowest average ratio of Mean Absolute Error (MAE) for surface roughness prediction in LUVAG.

关键词： Longitudinal ultrasonic grinding ensemble learning algorithm Alumina ceramic Support vector machine Surface roughness prediction

来源：评论

学校读者我要写书评

暂无评论

Wrapper Feature Subset Selection for Dimension Reduction Based on ensemble learning algorithm

引用

Procedia Computer Science 2015年 72卷 162-169页

作者： Rattanawadee Panthong Anongnart Srivihok Computer Science Department Kasetsart University Bangkok and 10900 Thailand

Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on the use of wrapper feature selection. This study use methods of sequential forward selection (SFS), sequential backward selection (SBS) and optimize selection (evolutionary) based on ensemble algorithms namely Bagging and AdaBoost by subset evaluations which are performed using two classifiers; Decision Tree and Naïve Bayes. Thirteen datasets containing different numbers of attributes and dimensions are obtained from the UCI Machine learning Repository. This study shows that the search technique using SFS based on the bagging algorithm using Decision Tree obtained better results in average accuracy (89.60%) than other methods. The benefits of the feature subset selection are an increased accuracy rate and a reduced run-time when searching multimedia data consisting of a large number of multidimensional datasets.

关键词： Feature selection ensemble learning algorithm dimension reduction classification data mining

来源：评论

学校读者我要写书评

暂无评论

Multistage ensemble learning Model With Weighted Voting and Genetic algorithm Optimization Strategy for Detecting Chronic Obstructive Pulmonary Disease

引用

IEEE ACCESS 2021年 9卷 48640-48657页

作者： Dhar, Joy Hatgobindapur MC High Sch Dept Informat Technol Bardhaman 713407 India

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung ailment and a significant cause of morbidity and fatality globally. The early detection of COPD can provide timely proper medication and reduce the mortality rate. To obtain proper treatment and lessen the death rate, this study proposes a novel ensemble model: the Multistage ensemble model (MSEN) with an optimized weighted voting technique to detect COPD early and help clinicians provide proper and timely medication. In this study, there are two pools of classifiers created in which four classifiers are placed in each pool. These two pools of classifiers are employed to form two weighted ensemble models based on a weighted voting strategy. This study combines those generated ensemble models using a weighted voting technique to form an MSEN model. The genetic algorithm is utilized to optimize the hyperparameters of each classifier in each pool. The weights of two generated ensemble models and each classifier are optimized using the grid search technique. This study employs the K-Nearest Neighbors approach to fill in the missing values, isolation forest to remove the outliers, and the LightGBM with Recursive Feature Elimination for feature selection. An evaluation of the suggested MSEN model is conducted on a real-world Exasens dataset to validate the suggested model's effectiveness which exhibits that the proposed model obtains better performance to detect COPD and provides superior performance than other machine learning models and existing benchmark techniques.

关键词： Diseases Lung Support vector machines Classification algorithms Medical diagnostic imaging Data models Computational modeling COPD detection ensemble learning algorithm genetic algorithm LightGBM algorithm machine learning recursive feature elimination voting classifier

来源：评论

学校读者我要写书评

暂无评论

A weighted ensemble learning-based autonomous fault diagnosis method for photovoltaic systems using genetic algorithm

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 144卷

作者： Eskandari, Aref Aghaei, Mohammadreza Milimonfared, Jafar Nedaei, Amir Amirkabir Univ Technol Dept Elect Engn Tehran Iran Norwegian Univ Sci & Technol NTNU Dept Ocean Operat & Civil Engn Alesund Norway Univ Freiburg Dept Sustainable Syst Engn INATECH Freiburg Germany

Conventional protection devices may not be able to diagnose the faults in Photovoltaic (PV) systems due to the nonlinear behavior of PV characteristics, its dependency on the operating environment, and operation of Maximum Power Point Tracking (MPPT) algorithms. To date, numerous studies have been carried out to over-come this challenge through Artificial Intelligence (AI) techniques. However, most of the AI-based techniques require a large dataset and also suffer overfitting problem. In this study, we propose an intelligent and automatic fault diagnosis method using less dataset for the training process through feature extraction and selection al-gorithms, as well as using an ensemble learning algorithm to classify open-circuit (OC) and line-line (LL) faults in PV systems. For this purpose, the proposed model firstly extracts the key features of the operating current and voltage of the PV arrays. Secondly, the Lasso penalty as an embedded feature selection technique is applied to determine the best subset of features. Thirdly, an ensemble learning algorithm consisting of three individual learning algorithms namely Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) is used in the classification stage to predict conditions of PV systems based on a weighted voting approach. Moreover, we apply a genetic algorithm to optimize the weights assigned to the algorithms in order to detect electrical faults in PV systems with higher accuracy. The experimental results demonstrate the proposed model is very efficient and reliable to diagnose open-circuit (OC) and line-line (LL) faults in PV arrays under any chal-lenging conditions with an accuracy of 99%.

关键词： Photovoltaic (PV) system Fault diagnosis Autonomous monitoring ensemble learning algorithm Genetic algorithm (GA)

来源：评论

学校读者我要写书评

暂无评论

Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning

引用

FRONTIERS IN MATERIALS 2025年 12卷

作者： Khan, Muhammad Salman Peng, Tianbo Khan, Muhammad Adeel Khan, Asad Ahmad, Mahmood Aziz, Kamran Sabri, Mohanad Muayad Sabri Abd EL-Gawaad, N. S. Tongji Univ Coll Civil Engn Dept Bridge Engn Shanghai Peoples R China Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai Peoples R China Univ Tenaga Nas Inst Energy Infrastruct Kajang Malaysia Univ Engn & Technol Peshawar Dept Civil Engn Bannu Campus Bannu Pakistan Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai Peoples R China Peter Great St Petersburg Polytech Univ St Petersburg Russia King Khalid Univ Muhayil Asir Appl Coll Abha Saudi Arabia

Accurately predicting key engineering properties, such as compressive and tensile strength, remains a significant challenge in high-performance concrete (HPC) due to its complex and heterogeneous composition. Early selection of optimal components and the development of reliable machine learning (ML) models can significantly reduce the time and cost associated with extensive experimentation. This study introduces four explainable Automated Machine learning (AutoML) models that integrate Optuna for hyperparameter optimization, SHapley Additive exPlanations (SHAP) for interpretability, and ensemble learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), and Categorical Gradient Boosting (CB). The resulting interpretable AutoML models O-RF, O-XGB, O-LGB, and O-CB are applied to predict the compressive and tensile strengths of HPC. Compared to a baseline model from the literature, O-LGB achieved significant improvements in predictive performance. For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. For tensile strength, it achieved a 99.41% improvement in MAE and a 96.67% reduction in RMSE, along with increases in R2. Furthermore, SHAP analysis identified critical factors influencing strength, such as cement content, water, and age for compressive strength, and curing age, water-binder ratio, and water-cement ratio for tensile strength. This approach provides civil engineers with a robust and interpretable tool for optimizing HPC properties, reducing experimentation costs, and supporting enhanced decision-making in structural design, risk assessment, and other applications.

关键词： high performance concrete machine learning ensemble learning algorithm SHAP Optuna compressive strength split tensile strength

来源：评论

学校读者我要写书评

暂无评论

Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models

引用

JOURNAL OF CLEANER PRODUCTION 2024年 442卷

作者： Huang, Peng Hou, Mengyao Sun, Tong Xu, Heng Ma, Chuanming Zhou, Aiguo China Univ Geosci Sch Environm Studies 388 Lumo Rd Wuhan 430000 Hubei Peoples R China Wuhan Univ Sch Resource & Environm Sci 299 Bayi Rd Wuhan 430000 Hubei Peoples R China Tianjin Univ Sch Civil Engn 135 Yaguan Rd Tianjin 300000 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol 15 Broadway Ultimo NSW 2007 Australia

The rapid economic growth of the Pearl River Delta, one of China's most economically developed areas, has led to a sharp increase in the demand for water resources, highlighting the importance of groundwater as a potential water source. However, the distribution of groundwater resources in this area is uneven, and excessive pollution emissions and seawater intrusion pose serious threats to groundwater quality. To address this challenge, this study employed a variety of machine learning and knowledge -driven models for groundwater potential mapping and vulnerability assessment. In terms of groundwater potential prediction, five machine learning algorithms were utilized, with the Light Gradient Boosting Machine (LightGBM) performing exceptionally well. The areas characterized by high and extremely high groundwater potential are predominantly concentrated in the central plains of the study area. In contrast, the groundwater potential is lower in the mountainous areas to the east, north, and south. Factors such as vadose zone, geomorphology, and land surface temperature were found to have the most significant impact on groundwater potential. For groundwater vulnerability assessment, a hybrid knowledge -driven framework was applied. The analysis revealed that the GALDIT model effectively assessed the risk of seawater intrusion. Near the Pearl River Estuary, the risk of seawater intrusion is notably high, while areas with low seawater intrusion risk are predominantly situated inland. Due to excessive pollution discharge, the DRASTIC model struggled to predict nitrate pollution. Additionally, the eXtreme Gradient Boosting (XGBoost) was used to analyze the impact of human activities on groundwater quality, with gross domestic product (GDP) and population density identified as key factors. Areas with a higher risk of nitrate pollution in groundwater are primarily located in industrial or densely populated areas where pollution sources are highly concentrated. This study prop

关键词： Groundwater potential Groundwater vulnerability ensemble learning algorithm DRASTIC GALDIT Pearl river delta

来源：评论

学校读者我要写书评

暂无评论

An ensemble-based transfer learning model for predicting the imbalance heart sound signal using spectrogram images

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 39923页

作者： Singh, Sinam Ajitkumar Devi, Ningthoujam Dinita Singh, Khuraijam Nelson Thongam, Khelchandra Reddy, Balakrishna Majumder, Swanirbhar NIT Manipur Dept Comp Sci & Engn Imphal 795004 Manipur India RIMS Imphal Dept Radiat Oncol Imphal 795004 Manipur India IIIT Nagpur Dept Elect & Commun Engn Nagpur 441108 Maharastra India RGMCET Dept Elect & Commun Engn Nandyal 518501 Andhra Pradesh India Tripura Univ Dept Informat Technol Suryamani Nagar Agartala 799022 Tripura India

Heart sound signal analysis is an important area in healthcare, and the detection of imbalanced heart sounds can provide valuable diagnostic information. However, due to heart sound variability, accurate prediction of imbalanced signals remains challenging. The issue of class inequality has received much attention from numerous scientific domains. The correct classification becomes increasingly challenging as data scale and data imbalance increase. Traditional classifiers tend to favor the dominant class and overlook the minority class, which is frequently considerably more significant when dealing with imbalanced learning problems. We propose an ensemble learning algorithm based on a transfer learning convolutional neural network (CNN) model to solve these challenges to predict imbalanced heart sound signals. We employ spectrogram images and STFT to extract the relevant features from Phonocardiogram (PCG) data. Our model leverages the pre-trained CNN architecture and fine-tunes it on the spectrogram images to improve the prediction performance. Moreover, we incorporate an ensemble approach to improve the model's robustness and accuracy. Our experimental results on a publicly available PhysioNet PCG dataset demonstrate that the proposed algorithm outperforms existing state-of-the-art methods in terms of accuracy, sensitivity, and specificity. The ensemble methodology comprising AlexNet, SqueezeNet, and VGG19 models was proposed and achieved the highest level of performance, resulting in an accuracy of 99.20% and a sensitivity rate of 99.47%. Our study showcases the potential of leveraging technological advancements to predict unbalanced Phonocardiogram (PCG) signals using spectrogram images. This research opens up promising avenues for future exploration in cardiac diagnostics. Specifically, the ensemble-based transfer learning model proposed in this study holds great promise.

关键词： Heard sound recording Spectrogram CNN PhysioNet-2016 Transfer learning ensemble learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of data-driven prediction model for CFRP-steel bond strength by implementing ensemble learning algorithms

引用

CONSTRUCTION AND BUILDING MATERIALS 2021年 303卷 124470-124470页

作者： Chen, Shi-Zhi Feng, De-Cheng Han, Wan-Shui Wu, Gang Changan Univ Highway Coll Xian 710064 Peoples R China Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 210096 Peoples R China

Bonding carbon fiber reinforced polymer (CFRP) laminates has been broadly utilized in steel structure rehabilitation. As for the final strengthened capacity, the bond strength between CFRP and steel usually plays a dominant role instead of the CFRP's mechanical property. However, the bond behavior of the CFRP-steel (CS) interface is very complicated with various failure modes and consequently the bond strength is hard to estimate leading to the CFRP strengthened steel structure insecure. Under this circumstance, in order to accurately predict the bond strength of CS, efficient data-driven models were developed through implementing ensemble learning (EL) algorithms named by "gradient boosting decision tree (GBDT)" and "random forest (RF)" as two representative ones on a collected CS single-shear test database. These models' performances on bond strength prediction were compared and also three representative machine learning algorithms "artificial neural network (ANN)", "support vector machine (SVM)" and "classification and regression tree (CART)" are utilized for validating the necessity. The comparison results indicate that the model generated by the GBDT algorithm attains the best accuracy for CS interfacial bond strength prediction (R2 = 0.98) among the ensemble and machine learning algorithms. Through model explaning analysis, the mechanism behind the GBDT based prediction model was also carefully verified. After these tests and analyses, the GBDT based model was proved to have the potential to facilitate the design and evaluation of CFRP strengthened steel structures.

关键词： CFRP-steel interface Bond strength prediction ensemble learning algorithm Gradient boosting decision tree Random Forest

来源：评论

学校读者我要写书评

暂无评论

The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

引用

Nuclear Science and Techniques 2024年第2期35卷 170-182页

作者： Gao-Yi Cheng Qian-Min Su Xi-Guang Cao Guo-Qiang Zhang School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate *** urgently need solutions to the dilemma of traditional particle identification *** study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate *** intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 ***-based machine learning algorithms and deep learning algorithms *** show excellent performance and generalization *** additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is *** learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

关键词： Heavy-ion collisions at low and intermediate energies Machine learning ensemble learning algorithm Particle identification Data imbalance

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：