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2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology(FMSMT 2017)

作者： Zhenxiang Lin Jinlin Guo Songyang Lao College of Information System and Management National University of Defense Technology

ISBN: (纸本)9781510840232

Automatic image annotation is a fundamental and challenging task in the field of image retrieval. This paper introduces the concept and process of random forest algorithm. According to the characteristics of the current annotation model, the random forest algorithm is applied in image annotation field. The experimental results show that the machine learning algorithm used in this paper is more effective than traditional algorithms, which has a higher accuracy and a short processing time.

关键词： ensemble learning algorithm Image annotation Machine learning

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KNN ensemble learning Integration algorithm Based on Three-Way Decision

KNN Ensemble Learning Integration Algorithm Based on Three-W...

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2022 International Joint Conference on Rough Sets

作者： Jia, Xinyuan Li, Yating Wang, Pengling Yuncheng Univ Maths & Informat Technol Sch Yuncheng 044000 Peoples R China Yuncheng Univ Mech & Elect Engn Dept Yuncheng 044000 Peoples R China

ISBN: (纸本)9783031212437;9783031212444

The KNN algorithm is affected by overlapping classification, unbalanced data, and K-value selection. It is difficult to apply to some environments with uncertain phenomena. At the same time, the three-way decision is a decision theory that conforms to the human cognitive model with subjective characteristics, so the idea of a three-way decision is introduced into the KNN ensemble learning algorithm and the KNN ensemble learning algorithm based on the three-way decision is proposed. Based on the KNN ensemble learning algorithm, the conditional Probability of each class is calculated and combined with the cost function, which is used to determine the positive domain, negative domain, and boundary domain in the three-way decision theory. This paper performs the threeway decision KNN ensemble learning classification on seven real UCI datasets. The experimental results show that it can effectively improve the classification accuracy and F1-score of the data.

关键词： KNN Three-way decision ensemble learning algorithm

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

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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

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

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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

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

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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

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Estimating forest growing stock volume using feature selection and advanced remote sensing algorithm

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2025年 37卷

作者： Zhao, Yabing Guo, Famiao Wang, Yin Huang, Jingxian Peng, Daoli Beijing Forestry Univ State Forestry & Grassland Adm Key Lab Forest Reso Beijing 100083 Peoples R China

Estimating dynamically and accurately forest growing stock volume (GSV) plays an essential role in ecosystem and forest resource monitoring. The focus of this research is to explore the synergistic and complementary potential of feature selection and machine learning to effectively improve the GSV remote sensing modeling performance. Based on multi-source remote sensing data, three variable selection methods and five modeling algorithms were used to evaluate the GSV estimation performance. The results indicated that: (1) Combining the multi-source images improved the GSV prediction performance, and the model constructed using optical and radar images produced higher estimation accuracy than optical sensors alone. (2) Boruta method retained the richest variable information for GSV estimation, which was better than stepwise regression analysis (SRA) and recursive feature elimination (RFE). (3) ensemble learning has advantages in GSV estimation, categorical boosting (CatBoost) improved the GSV estimation performance compared with other modeling algorithms. (4) The CatBoost effectively performed in GSV prediction and alleviated overestimation and underestimation to some degree. This research proved the positive role of feature selection and machine learning in GSV estimation based on multi-source remote sensing data, which will help to further monitor GSV in large areas of forests.

关键词： Forest growing stock volume Multi-source remote sensing dataset Feature selection Machine learning ensemble learning algorithm

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

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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)

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

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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

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C-MEL: Consensus-Based Multiple ensemble learning for Indoor Device-Free Localization Through Fingerprinting

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IEEE ACCESS 2024年 12卷 166381-166392页

作者： Suroso, Dwi Joko Adiyatma, Farid Yuli Martin Univ Gadjah Mada Dept Nucl Engn & Engn Phys Yogyakarta 55281 Indonesia Sch Engn King Mongkuts Inst Technol Ladkrabang Bangkok 10520 Thailand

The rise of location-aware services is enhancing the effectiveness of our daily tasks, especially within indoor environments where most activities take place. Wireless indoor localization systems are the predominant method for estimating locations indoors. These systems utilize two primary approaches: device-based and device-free. Device-based techniques are attracting considerable research attention due to their ability to offer highly accurate localization in most scenarios. Conversely, device-free techniques are increasingly popular because they can determine a target's location without the target carrying a device. This capability makes them suitable for certain applications such as elderly monitoring and intruder tracking. The most popular technique for both approaches is fingerprinting, which uses the uniqueness of spatial information to predict a target's location. This spatial information is stored in a fingerprint database, containing locations and their associated parameters. However, in device-free methods, the fingerprint technique encounters challenges in accurately recognizing the complexity of each parameter combination pattern, thus impacting the accuracy of the estimation. To overcome this issue, we introduce a novel indoor device-free localization (IDFL) pattern matching algorithm named Consensus-based Multiple ensemble learning (C-MEL). This algorithm incorporates consensus strategies, i.e., majority voting and average strategy, to integrate outputs from various ensemble learning algorithms, such as Random Forest, Gradient Boosting, XGBoost, and LightGBM. We validate our algorithm in an 18 m2 office space featuring stainless steel partitions, tables, chairs, and cabinets. Experimental results show that C-MEL using the average strategy (C-MEL-AV) enhances accuracy by up to 44.51%, 11.26%, and 37.85%, while C-MEL with majority voting (C-MEL-MV) improves by up to 40.56%, 4.95%, and 33.44% compared to Decision Tree, Gradient Boosting, and 1D CNN-BLSTM

关键词： Location awareness Accuracy Fingerprint recognition Random forests ensemble learning Wireless fidelity Boosting Buildings Classification algorithms Wireless communication Indoor device-free localization fingerprinting C-MEL consensus strategy ensemble learning algorithm

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

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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

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