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A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2023年第2期22卷 1273-1289页

作者： Sheng, Wenjuan Liu, Yutao Soeffker, Dirk Shanghai Univ Elect Power Coll Automat Engn Shanghai 200090 Peoples R China Univ Duisburg Essen Chair Dynam & Control Duisburg Germany

adaptive boosting (AdaBoost) algorithms fuse multiple weak classifiers to generate a strong classifier by adaptively determining the fusion weights of the weak classifiers. According to incorrect or correct classification results, sample weights become larger or smaller. However, this weight update scheme neglects valuable information in the results. Moreover, an important requirement for weak classifiers is an accuracy higher than random guessing. This requirement is likely to lead to an unexpected result. This means that several generated weak classifiers with similar classification results cannot learn from each other. Consequently, the advantage of fusing multiple weak classifiers disappears. The classification and therefore distinction of different failure modes in materials is a typical task for classical nondestructive testing approaches as well as for new approaches based on machine learning methods. In the case different approaches can be applied, the main question is, which and how tuned approaches provide the best results in terms of accuracy or similar metrics. In the multi-damage classification task distinguishing different physical failure mechanisms in Carbon Fiber Reinforced Polymer (CFRP), the above two aspects complicate the application of AdaBoost algorithms. To improve the results, a novel AdaBoost with distance-based weighted least square support vector machine (WLSSVM) and filter factor is proposed. The distance-based WLSSVM is employed to increase the diversity of weak classifiers, the distances of the classification plane and samples are used to measure the classification task. The filter factor is proposed to filter out unnecessary classifiers contributing less to the final decision. The experimental results demonstrate that the improved AdaBoost schemes with distance-based WLSSVM and filter factor outperform state-of-the-art algorithms. The effects of the scheme using the new weighted update and the filter factor on the algorithm are discus

关键词： adaptive boosting algorithm weighted least square support vector machine carbon fiber reinforced polymer damage classification

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Improving the performance of an unmixing model in sediment source apportionment using synthetic sediment mixtures and an adaptive boosting algorithm

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CATENA 2022年 217卷

作者： Zhao, Yang Gao, Guanglei Ding, Guodong Zhou, Qizhi Zhang, Ying Wang, Jiayuan Zhou, Jinxing Beijing Forestry Univ Sch Soil & Water Conservat Yanchi Res Stn Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab State Forestry & Grassland Adm Soil & Wate Beijing 100083 Peoples R China Beijing Forestry Univ Engn Res Ctr Forestry Ecol Engn Minist Educ Beijing 100083 Peoples R China Beijing Forestry Univ 35 East Tsinghua Rd Beijing 100083 Peoples R China

Tracing technology is increasingly being used in fluvial and aeolian sediment provenance assessments. Using synthetic sample mixtures in validations of unmixing model performance is becoming a standard step in sediment source fingerprinting. With tracer variability fully considered in the sources and target sediment, this study explored a semiempirical modelling strategy based on virtual sediment mixtures and the adaptive boosting (AdaBoost) algorithm. The obtained integrated unmixing model contained multiple composite fingerprints, and the weight coefficient was obtained from the iterative process. The performance of the integrated unmixing model was compared with that of unmixing models applying single or equal-weighted multiple composite fingerprints. All generated virtual sediment mixtures (280) were split into a training dataset (240) and a test dataset (40) to validate the generalization ability of the models. The results showed that the integrated unmixing model achieved better performance than the unmixing models with a single composite fingerprint (basic models). The integrated unmixing model yielded an average mean absolute error (A-MAE) of 5.51% for training data and 5.72% for test data, and it achieved better accuracy than the best basic model (6.23% on the training and 6.75% on the test dataset, composite fingerprint C-1) or equal-weighted model (6.32% on the training and 6.41% on the test dataset, average of 42 basic models). The robustness of the model accuracy was also improved with the AdaBoost algorithm. The modelling approach proposed in this study has the potential to maximize the use of all tracer information and further improve the reliability of sediment fingerprinting.

关键词： Sediment fingerprinting Virtual sediment mixtures adaptive boosting algorithm Unmixing model

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Stacking Ensemble algorithm to Predict Re-keying in Group Key Management

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-15页

作者： Kumari, Prity Singh, Karam Ratan Kumar, Ranjan Natl Inst Technol Arunachal Pradesh Dept Basic & Appl Sci Jote 791113 India Natl Inst Technol Arunachal Pradesh Dept Mech Engn Jote 791113 India

Group key management offers a flexible and reliable security mechanism for secure communication in wireless sensor network by assisting with suitable adjustments of the number of keys per node and the number of re-keying messages. In this article, we obtained a datasets using a projective plane after removing a single element. We employ a stacking ensemble algorithm to predict the re-keying value in a projective plane. To improve the performance of the prediction in the stacking model, adaptive boosting and random forest models are chosen as base learners, and for the meta-learner, linear regression is chosen. We observed that the stacking ensemble algorithm demonstrated higher accuracy compared to individual models. The accuracy of the stacking ensemble algorithm is found to be 0.9999, with MAE, MSE, and RMSE values of 0.0026, 0.0000, and 0.0030 respectively.

关键词： adaptive boosting algorithm Group key management Linear regression Projective plane Random forest Re-keying Stacking ensemble algorithm Wireless sensor network

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Measurement of gas volume fraction in gas-liquid two-phase flow using arrayed fiber-optic probes combined with the PSO-BP-AdaBoost algorithm

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OPTICAL FIBER TECHNOLOGY 2025年 93卷

作者： Xing, Wenju Gao, Hong Qiao, Xueguang Xian Shiyou Univ Shaanxi Prov Oil & Gas Resource Fiber Opt Detect E Sch Sci Xian 710065 Peoples R China Northwest Univ Sch Phys Xian 710069 Peoples R China

For the measurement of gas volume fraction in natural gas wells, a strategy based on the fusion of arrayed fiberoptic probes (AFOP) and artificial intelligence algorithms is proposed to enhance the precision and efficiency of gas volume fraction monitoring. As a key front-end component for obtaining gas phase information, AFOP determines the optimal structure by analyzing its performance metrics in bubble capture and its interference with fluid flow. A back-end gas volume fraction prediction model was constructed using a machine learning algorithm. The model first uses a particle swarm optimization (PSO) algorithm to enhance the backpropagation (BP) neural network as a weak predictor and then integrates multiple weak predictors through the adaptive boosting (AdaBoost) algorithm to create a strong predictor. The experimental results show that compared with the support vector machine (SVM), BP neural network, and PSO-BP neural network, the PSO-BP-AdaBoost algorithm has advantages in prediction precision, with a maximum relative error of only 0.14 %, providing a more effective solution for research and application in related fields.

关键词： Arrayed fiber-optic probes Gas volume fraction Backpropagation neural network Particle swarm optimization algorithm adaptive boosting algorithm

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A Practical Method for Floor Water Inrush Prediction Using a Hybrid Artificial Intelligence Model and GIS

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MINE WATER AND THE ENVIRONMENT 2023年第2期42卷 220-229页

作者： Liu, Weitao Han, Mengke Shandong Univ Sci & Technol Coll Safety & Environm Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

Predicting floor water inrush has become increasingly challenging as coal is being mined at greater depths. We established a practical predictive method using a hybrid artificial intelligence (AI) model and geographic information system (GIS) techniques. The hybrid model is a classifier that mixes a back propagation neural network (BPNN) with an adaptive boosting algorithm (AdaBoost). To assess the effectiveness of the model, 33 borehole data points with known water inrush results in the Yangcheng coal mine were used as data samples to train and test the model. The outcomes demonstrated a predictive accuracy of 100%, far exceeding the accuracy and stability of the BPNN classifier alone using the same parameters. Then, GIS techniques were used to extend the approach throughout the mining region;the greatest risk was shown to be in the middle of the area. Given the limited data set, errors may exist in extending the risk predictions for the entire mining area, so more data needs to be collected to ensure the accuracy of subsequent predictions. Still, we believe that the methods and steps adopted in this study can be used to create practical predictive models in different mining regions.

关键词： Back propagation neural network adaptive boosting algorithm Geographic information system Mining

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The potential of depressive symptoms to identify cognitive impairment in ageing

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EUROPEAN JOURNAL OF AGEING 2025年第1期22卷 1-11页

作者： Alexopoulos, Panagiotis Bountoulis, Christos Katirtzoglou, Everina Kosmidis, Mary H. Siarkos, Kostas Yannakoulia, Mary Dardiotis, Efthimios Skondra, Maria Hadjigeorgiou, Georgios Perneczky, Robert Sakka, Paraskevi Georgiou, Eleni-Zacharoula Charalampopoulou, Marina Felemegkas, Panagiotis Leroi, Iracema Batsidis, Apostolos Perna, Laura Politis, Antonios Scarmeas, Nikolaos Economou, Polychronis Univ Patras Patras Univ Gen Hosp Mental Hlth Serv Dept MedSch Hlth Sci Patras 26504 Greece Trinity Coll Dublin Global Brain Hlth Inst Sch Med Dublin Ireland Tech Univ Munich Sch Med Dept Psychiat & Psychotherapy Fac Med Munich Germany Patras Dementia Day Care Ctr Patras Greece Univ Piraeus Dept Business Adm Piraeus Greece Natl & Kapodistrian Univ Athens Eginit Hosp Sch Med Dept Psychiat 1 Athens Greece Aristotle Univ Thessaloniki Sch Psychol Lab Cognit Neurosci Thessaloniki Greece Harokopio Univ Dept Nutr & Dietet Athens Greece Univ Thessaly Larissa Univ Gen Hosp Dept Pediat Gen Univ Hosp Larissa Larisa 41110 Larissa Greece Univ Cyprus Med Sch Dept Neurol Nicosia Cyprus Ludwig Maximilians Univ Munchen Univ Hosp Div Mental Hlth Older Adults & Alzheimer Therapy & Dept Psychiat & Psychotherapy Munich Germany Imperial Coll Sci Technol & Med Fac Med Sch Publ Hlth Ageing Epidemiol AGE Res Unit London England German Ctr Neurodegenerat Dis DZNE Munich Munich Germany Munich Cluster Syst Neurol SyNergy Munich Germany Univ Sheffield Sheffield Inst Translat Neurosci SITraN Sheffield England Athens Assoc Alzheimers Dis & Related Disorders Maroussi Greece Univ Ioannina Dept Math Ioannina Greece Max Planck Inst Psychiat Dept Genes & Environm Munich Germany Johns Hopkins Med Sch Dept Psychiat Div Geriatr Psychiat & Neuropsychiat Baltimore MD USA Natl & Kapodistrian Univ Athens Med Sch Eginit Hosp Dept Neurol 1 Athens Greece Columbia Univ Taub Inst Res Alzheimers Dis & Ageing Brain Gertrude H Sergievsky Ctr Dept Neurol New York NY USA Univ Patras Sch Engn Dept Civil Engn Patras Greece

Depressive symptoms are common in mild cognitive impairment (MCI), dementia caused by Alzheimer's disease (AD dementia) and in cognitively unimpaired older adults. However, it is unclear whether they could contribute to the identification of cognitive impairment in ageing. To assess the potential utility of depressive symptoms to distinguish between healthy cognitive ageing and MCI and AD dementia. The diagnostic workup of the cognitive function of 1737 older cognitively unimpaired individuals, 334 people with MCI and 142 individuals with AD dementia relied on a comprehensive neuropsychiatric assessment, including the Mini Mental State Examination (MMSE). Depressive symptoms were tapped with the 15-item Geriatric Depression Scale (GDS). Proportional odds logistic regression (POLR) models and the machine learning technique adaptive boosting algorithm (AdaBoost) were employed. Stratified repeated random subsampling (stratified bootstrap resampling) was used to recursive partitioning to training- and validation set (70/30 ratio). The average accuracy of the POLR models for the GDS total score in distinguishing between cognitive impairment and healthy cognitive ageing exceeded 78% and was inferior to that of MMSE. Of note, the sensitivity of GDS total score was very low. By employing the AdaBoost algorithm and considering GDS items separately, the average accuracy was higher than 0.72 and comparable to that of the MMSE, while sensitivity- and specificity values were more balanced. The findings of the study provide initial evidence that depressive symptoms may contribute to distinguishing between cognitive impairment and cognitively healthy ageing.

关键词： Geriatric depression scale adaptive boosting algorithm Mild cognitive impairment Dementia due Alzheimer's disease

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Iterative Weak Learnability and Multiclass AdaBoost 24

Iterative Weak Learnability and Multiclass AdaBoost

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Cho, In-Koo Libgober, Jonathan A. Ding, Cheng Emory Univ Atlanta GA 30322 USA Hanyang Univ Seoul South Korea Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9798400704901

We propose an efficient boosting algorithm for multiclass classification, called ***, that extends SAMME [14] and AdaBoost [4]. The algorithm iteratively applies the weak learnability condition of SAMME to eliminate classes to find the correct classificiation. The iterative weak learnability is a sufficient and necessary condition for boostability, but it is also easier to validate than the EOR criterion of *** [9]. We show that the training error of *** vanishes at the exponential rate, while the generalization error converges to zero at the same rate as AdaBoost. *** numerically outperforms SAMME and achieves performance comparable to *** on benchmark datasets.

关键词： adaptive boosting algorithm Iterative Weak Learnability Boostability Multiclass Classification

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Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling

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ENERGY REPORTS 2022年 8卷 6672-6686页

作者： Xiao, Ling Li, Miaotong Zhang, Shenghui Xuzhou Inst Technol Sch Math & Stat Xuzhou 221018 Peoples R China Chongqing Univ Posts & Telecommun Sch Econ & Management Chongqing 400065 Peoples R China Univ Macau Dept Comp & Informat Sci Org State Key Lab Internet Things Smart City Zhuhai 999078 Macao Peoples R China

Short-term power load forecasting plays a vital role in the planning of distribution network and the development of social economy. Many researchers have devoted their attention to construct point forecasting models. However, the traditional point forecasting regards the forecasting result as a deterministic variable. Since the deviation existed in load forecasting is simply unavoidable and significant, which has great volatility and randomness, point forecast methods are difficult to capture the fluctuation of power load. Probability forecast models are proposed to obtain the uncertain information in the load power. In this paper, firstly, some error information is obtained from the deterministic forecasting results of point forecasting;secondly, the interval of time series data is divided according to the deterministic error information;finally, the Bootstrap method is used to estimate the confidence interval of the deteministic error information to obtain the uncertainty information in the power load data. The instability and randomness of the deterministic error are otained by combining the interval forecasting method so as to improve the accuracy of power load forecasting. Therefore, probability forecasting is combined with point forecasting to obtain more accurate results. The proposed model is used to forecast power load for Queensland, Australia and capital district of New York State. The experimental results show that the proposed method performs better than other comparative models. The experimental results show that the ELM-AdaBoost model has better forecast performance in both long-term and short-term load datasets, and can overcome the seasonality of power load time series data. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Power load interval forecasting Extreme learning machine Nonparametric Bootstrap sampling adaptive boosting algorithm

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Mechanical properties and global warming potential predictions for high-performance concrete through machine learning models integrated with meta-heuristic algorithms

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MATERIALS TODAY COMMUNICATIONS 2024年 41卷

作者： Song, Yuhua Chongqing Vocat Coll Appl Technol Sch Intelligent Construct & Management Chongqing 401520 Peoples R China

Recent developments in high-performance concrete (HPC) have made it a high-tech material with enhanced durability and properties, and it is a more ecological material with a longer life cycle. Conventional approaches to assessing HPC properties and environmental effects may lack precision in predicting the effects of additional cementitious materials. adaptive boosting algorithm (ADA) and Stochastic Gradient boosting (SGB) have emerged as promising approaches for predicting HPC properties and environmental effects, offering more accurate and efficient alternative methods. In this study, the ADA and SBG models have been employed to predict the two essential HPC mechanical properties, including compressive and tensile strengths (CS and TS) and Global Warming Potential (GWP) by coupling with meta-heuristic algorithms, namely Stadium Spectators Optimizer (SSO), Golden Jackal algorithm (GJA), Brown-bear Optimization algorithm (BOA), and Golf Optimization algorithm (GOA)(sic) for developing hybrid and ensemble prediction approaches. The study assessed the effectiveness of different models for predicting HPC properties and GWP by employing various metrics. The SGSBGG framework showcased remarkable precision and performance compared to other models in the prediction of CS, TS, and GWP. It outshone its counterparts by achieving the highest prediction accuracy, securing R-2 values of 0.994, 0.994, and 0.995 for CS, TS, and GWP predictions, respectively. Additionally, it demonstrated unparalleled accuracy with the lowest modeling errors, registering an RMSE of 1.306 for CS, 0.097 for TS, and 4.469 for GWP. Despite the clear superiority of the SGSBGG model, it is noteworthy that the SGSO, as a hybrid model, delivered notably compatible results.

关键词： High-Performance Concrete Strength Properties Global Warming Potential adaptive boosting algorithm Stochastic Gradient boosting Meta-heuristic algorithms

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Prediction of Coal Seam Permeability by Hybrid Neural Network Prediction Model

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JOURNAL OF ENERGY ENGINEERING 2024年第4期150卷

作者： Wang, Jian Zhao, Mifu Wang, Bowen Wang, Yahua Yang, Gang Ma, Tengfei Xu, Jiafang China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Sinopec Dept Field Dev Northeast Petr Branch Changchun 130062 Peoples R China China United Coalbed Methane Co Ltd 21 Jiuxianqiao Rd Beijing 100011 Peoples R China

Coalbed methane (CBM) productive efficiency and coal mine disasters such as gas outbursts and water inrush are closely correlated with coal seam permeability. Effective prediction of coal seam permeability can provide guidance for CBM production and prevention of coal mine disasters. In this research, a hybrid neural network prediction model integrating a genetic algorithm, an adaptive boosting algorithm, and a back propagation neural network was developed to predict coal seam permeability. Additional momentum and variable learning rate algorithms were used to improve the learning rate and accuracy of the model, and the model structure was optimized, including the number of hidden layer nodes and the transfer function. The input parameters of the prediction model included gas pressure, compressive strength, reservoir temperature, and effective stress. The corresponding output parameter was coal seam permeability. The correlation between the parameters was calculated. Additionally, a comparative analysis between the proposed prediction model and four other prediction models was carried out to demonstrate the advantages of the proposed model. The results indicated that the correlations between compressive strength, gas pressure, reserve temperature, effective stress, and coal seam permeability were 0.334, -0.148, -0.406, and -0.785, respectively. The proposed prediction model had high accuracy compared with the other prediction models, and its coefficient of determination and root mean squared error were 0.999 and 0.021. Thus, the model can predict coal seam permeability more accurately.

关键词： Coal seam permeability Back propagation neural network Additional momentum algorithm Variable learning rate algorithm Genetic algorithm adaptive boosting algorithm

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