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Data-Driven Insights: boosting algorithms to Uncover Electricity Theft Patterns in AMI

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Khan, Inam Ullah Ali, Arshid Taylor, C. James Ma, Xiandong Southern Methodist Univ Bobby B Lyle Sch Engn Dallas TX 75205 USA South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA Univ Lancaster Sch Engn Lancaster LA1 4YW England

This study introduces a sophisticated supervised machine learning method for electric theft detection utilizing a customized histogram gradient boosting (HGB) algorithm. Comprehensive preprocessing, including imputation, normalization, outlier management, and resampling, ensures that the time-series data are accurately prepared for analysis. The synthetic minority oversampling technique-edited nearest neighbor (SMOTE-ENN) algorithm corrects class imbalances, preparing the data for the feature optimization stage, in which key features are selected and extracted. The HGB algorithm, enhanced through Bayesian optimization, is central to the training process, resulting in a model that precisely classifies electricity consumption patterns as genuine or fraudulent. The robustness of the model is evaluated against other recognized boosting methods, such as adaptive boosting (ADB), gradient boosting decision tree (GBDT), and LightGBM, alongside various ensemble and traditional machine learning models. Utilizing key performance metrics such as accuracy, F1-score, and area under the curve (AUC) for validation, the proposed model yields very promising results, with 93% accuracy, 95% F1-score, and 98% AUC, outperforming the comparison group under similar dataset and hyperparameter conditions. This underscores the model's potential as a highly accurate tool for combating electricity theft within an advanced metering infrastructure (AMI).

关键词： Feature extraction boosting Electricity Meters Computational modeling Classification algorithms Smart meters Smart grids Costs Accuracy Advanced metering infrastructure (AMI) boosting algorithms class balancing electricity theft detection (ETD) feature engineering smart grid

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boosting algorithms Empowering Heart Disease Prediction for Enhanced Medical Accuracy 3

Boosting Algorithms Empowering Heart Disease Prediction for ...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Sharma, Urvashi Saxena, Kanak Samrat Ashok Technological Institute Computer Science & Engineering Department Vidisha India Samrat Ashok Technological Institute Head Computer Science & Engineering Department Vidisha India

ISBN: (纸本)9798331523893

Heart disease remains a leading cause of mortality worldwide, necessitating accurate and reliable predictive models to aid early diagnosis and treatment. Traditional machine learning methods like LR and DT Classifiers offer reasonable recall rates but suffer from lower precision and accuracy. To address this challenge, we employed advanced boosting algorithms, specifically GradientboostingClassifier and XGBClassifier, to enhance predictive accuracy in heart disease detection. Our study shows that these algorithms achieve accuracy rates above 93%, with precision exceeding 52%, and a significantly low False Positive Rate below 0.5%. However, the trade-off involves lower recall rates, suggesting some true cases remain undetected. boosting algorithms thus provide a robust approach for heart disease prediction, though attention to false negatives is essential for comprehensive application. © 2025 IEEE.

关键词： boosting algorithms Gradient boosting Classifier Heart Disease Prediction Machine Learning Medical Accuracy XGBClassifier

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Comparative analysis of boosting algorithms for predicting personal default

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COGENT ECONOMICS & FINANCE 2025年第1期13卷

作者： Nguyen, Nhat Ngo, Duy Ho Chi Minh Univ Banking Ho Chi Minh Vietnam

Accurately predicting personal default risk is crucial for financial institutions to manage credit risk effectively. This study conducts a comparative analysis of the performance of boosting algorithms, including AdaBoost, XGBoost, LightGBM, and CatBoost, in predicting personal defaults. The dataset used in the study comprises 7,542 individual customers collected from Vietnamese commercial banks and financial institutions between 2014 and 2022, with 12 features related to the financial and demographic characteristics of the borrowers. All customer-related information is fully anonymized and encrypted during the data collection process to ensure compliance with research ethics. The predictive models are evaluated based on six criteria: Accuracy, Precision, Sensitivity, Specificity, F1 score, and AUC. The results indicate that the LightGBM model has the best performance, demonstrating the ability to efficiently handle large and complex datasets. Additionally, the study identifies the five most significant factors influencing personal default risk: Monthly Liability, Credit Balance, Credit History Length, Max Credit Limit, and Yearly Income. However, the study's limitations in the size and scope of the dataset may reduce the generalizability of the results when applied to other regions. These findings provide valuable insights that help financial institutions enhance their strategies for managing credit risk effectively.

关键词： Personal default prediction boosting algorithms feature importance analysis machine learning financial institutions

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An Empirical Study on the Performance of Cost-Sensitive boosting algorithms with Different Levels of Class Imbalance

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-12页

作者： Yin, Qing-Yan Zhang, Jiang-She Zhang, Chun-Xia Liu, Sheng-Cai Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Cost-sensitive boosting algorithms have proven successful for solving the difficult class imbalance problems. However, the influence of misclassification costs and imbalance level on the algorithm performance is still not clear. The present paper aims to conduct an empirical comparison of six representative cost-sensitive boosting algorithms, including AdaCost, CSB1, CSB2, AdaC1, AdaC2, and AdaC3. These algorithms are thoroughly evaluated by a comprehensive suite of experiments, in which nearly fifty thousands classification models are trained on 17 real-world imbalanced data sets. Experimental results show that AdaC serial algorithms generally outperform AdaCost and CSB when dealing with different imbalance level data sets. Furthermore, the optimality of AdaC2 algorithm stands out around the misclassification costs setting: C-N = 0.7, C-p = 1, especially for dealing with strongly imbalanced data sets. In the case of data sets with a low-level imbalance, there is no significant difference between the AdaC serial algorithms. In addition, the results indicate that AdaC1 is comparatively insensitive to the misclassification costs, which is consistent with the finding of the preceding research work.

关键词： boosting algorithms EMPIRICAL research PROBLEM solving PERFORMANCE evaluation DATA analysis SET theory

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Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development

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ENVIRONMENTAL POLLUTION 2024年 341卷 123017页

作者： Nguyen, Duc-Viet Park, Jihae Lee, Hojun Han, Taejun Wu, Di Univ Ghent Ctr Environm & Energy Res Global Campus Incheon 21985 South Korea Univ Ghent Ctr Adv Proc Technol Urban Resource Recovery CAPTU Dept Green Chem & Technol B-9000 Ghent Belgium Univ Ghent Dept Anim Sci & Aquat Ecol B-9000 Ghent Belgium Univ Ghent Bio Environm Sci & Technol BEST Lab Global Campus119-5 Songdomunhwa Ro Incheon 21985 South Korea

Trace heavy metals have a tendency to persist in the effluent of industrial wastewater treatment facilities, leading to toxic effects on downstream water bodies. Traditional assessment methods relied on animal testing, but ethical concerns have rendered them unacceptable. An alternative solution is to evaluate wastewater toxicity using trophic-level aquatic organisms as bioassays. However, these bioassay methods involve costly and timeconsuming chemical and biological analytical experiments. In this study, an artificial intelligence-powered water quality assessment (AiWA) approach is proposed for predicting industrial effluent ecotoxicity to further enhance the quick and cost-effective ecotoxicity assessment process. Initially, 99 samples were collected from industrial wastewater treatment plants representing 21 different industries in the Republic of Korea. Fourteen parameters were measured, encompassing both physicochemical and ecotoxicological aspects. boosting algorithms, especially extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost), were employed for model development. XGBoost outperformed AdaBoost in terms of model performance. Feature selection analysis revealed that conductivity, copper, lead, selenium, pH, and zinc concentrations were the most suitable inputs for training the boosting model. The innovated XGBoost-based AiWA model demonstrated significantly higher performance (i.e., up to 80%) compared to conventional models with an R2 value of exceeding 0.94 and root mean square error of 3.5 toxicity unit for predicting the integrated toxicity unit (ITU). Additionally, pH and conductivity emerged as crucial indicators for reflecting ecotoxicity levels. Specially, this case study indicated that non-toxic/directly dischargeable levels (TU <= 1) were achieved when the pH ranged from 6.8 to 8.4 and the conductivity remained below 1651 mu S/cm. These findings are expected to facilitate rapid and cost-effective detection of heavy metal ecotoxici

关键词： Artificial intelligence boosting algorithms Heavy metals Ecotoxicity prediction and classification Industrial wastewater management

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Analysis of uniform resource locator using boosting algorithms for forensic purpose

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COMPUTER COMMUNICATIONS 2022年 190卷 69-77页

作者： Apoorva, K. A. Sangeetha, S. Natl Inst Technol Dept Comp Applicat Tiruchirapalli Tamil Nadu India

Innovations are taking up new roles in all fields. It still has a crucial role in Internet technology, as the ease with which the Internet is available everywhere and accessible from any device has resulted in a slew of cyber-attacks., A prevalent scenario during and before a pandemic is phishing, which is accomplished by smartly altering the URL as a legitimate one and then redirecting the user to other sites and extracting personal information. The benchmark URL datasets used for the study considering an equal balance between phishing/ malicious URLs and benign/ legitimate URLs. URLs are parsed in this procedure to extract valuable elements that aid in the identification of URL phishing. Our research emphasized using different machine learning boosting algorithms such as Extreme Gradient boosting, Light Gradient boosting, Adaptive boosting, and Gradient boosting and have achieved an accuracy of more than 98% for most of the algorithms considered.

关键词： Phishing Machine learning boosting algorithms URL features Cyber-attacks

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Improved boosting algorithms using confidence-rated predictions

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MACHINE LEARNING 1999年第3期37卷 297-336页

作者： Schapire, RE Singer, Y AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA

We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. This method also suggests a technique for growing decision trees which turns out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multi-label case in which each example may belong to more than one class. We give two boosting methods for this problem, plus a third method based on output coding. One of these leads to a new method for handling the single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.

关键词： boosting algorithms multiclass classification output coding decision trees

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Application of deep learning in civil engineering: boosting algorithms for predicting strength of concrete

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 9109-9122页

作者： Xie, Canrong Wang, Jianjun Wu, Zhiwen Nie, Shaojun Hu, Yichan Huang, Sheng Guangxi Rd & Bridge Engn Grp Co Ltd Nanning Peoples R China Guangxi Univ Coll Civil Engn & Architecture Key Lab Disaster Prevent & Struct Safety Minist Nanning Peoples R China

Machine learning (ML) has been applied in civil engineering to predict the compressive strength of concrete with high accuracy. In this paper, five boosting ensemble algorithms, i.e., XGBoost, AdaBoost, GBDT, LightGBM, and CatBoost, were used to predict the compressive strength of high-performance concrete (HPC). The models were evaluated using performance indicators such as R-2, root mean square error (RMSE), and mean absolute error (MAE). The results showed that the CatBoost model had the highest accuracy with a R-2 (0.970) and a RMSE (2.916). The prediction accuracy of the model was increased through hyperparameter optimization, which got a higher with a R-2 (0.975) and a RMSE (2.863). Meanwhile, the SHapley Additive exPlanations (SHAP) method was used to explain the output results of the optimal model (CatBoost), which generated explainable insights that further revealed the complex relationship between the prediction model parameters. The results showed that AGE, W/B, and W/C had the most impact on high-performance concrete compressive strength (HPCCS) prediction, which was similar to the results of sensitivity analysis. This study provided a theoretical basis and technical guidance for developing the mix design of a new high-performance concrete (HPC) system. In the future, the interpretable results of the model output should be iteratively checked and validated in the actual laboratory in order to provide guidance for engineering practice.

关键词： High-Performance Concrete (HPC) compressive strength machine learning boosting algorithms game theory

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Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2004年第3期12卷 296-308页

作者： del Jesus, MJ Hoffmann, F Navascués, LJ Sánchez, L Univ Jaen Dept Comp Sci Jaen Spain Univ Dortmund D-44227 Dortmund Germany Univ Oviedo Dept Comp Sci E-33204 Oviedo Spain

This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. Connections between iterative learning and boosting are analyzed in terms of their respective structures and the manner these algorithms address the cooperation-competition problem. The results are used to explain some properties of the former method. The evolutionary boosting scheme is applied to approximate and descriptive fuzzy-rule bases. The advantages of boosting fuzzy rules are assessed by performance comparisons between the proposed method and other classification schemes applied on a set of benchmark classification tasks.

关键词： boosting algorithms evolutionary algorithms fuzzy-rule-based classifiers iterative learning

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Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data

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DESALINATION AND WATER TREATMENT 2022年 276卷 28-39页

作者： Wazirali, Raniyah Abujazar, Mohammed Shadi S. Abujayyab, Sohaib K. M. Ahmad, Rami Fatihah, Suja Kabeel, A. E. Karaagac, Sakine Ugurlu Abu Amr, Salem S. Alazaiza, Motasem Y. D. Bashir, Mohammed J. K. Sokar, Ibrahim Y. Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia Al Aqsa Univ Al Aqsa Community Intermediate Coll PB 4051 Gaza Palestine Karabuk Univ Fac Engn Dept Environm Engn TR-78050 Karabuk Turkey Int Coll Engn & Management Fire Safety Engn Muscat 112 Oman Amer Univ Emirates Coll Comp Informat Technol Dubai 503000 U Arab Emirates Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Civil Engn Bangi 43600 Selangor Malaysia Tanta Univ Fac Engn Mech Power Engn Dept Tanta Egypt Int Coll Engn & Management 111 St Muscat Oman A Sharqiyah Univ Coll Engn Dept Civil & Environm Engn Ibra 400 Oman Univ Tunku Abdul Rahman Fac Engn & Green Technol Dept Environm Engn Kampar 31900 Perak Malaysia Gaza Univ Fac Comp Sci & Informat Technol Gaza Palestine

Solar energy has recently become a viable option for desalinating seawater, primarily in arid regions. However, increasing the productivity of solar still by integrating experimental base and modelling methods is still subject to prediction errors;therefore, the main objective of this research is to postulate and test boosting algorithms for predicting the efficiency and productivity of the system. Five boosting regressors were deployed and evaluated: categorical boosting, adaptive boosting, extreme gradient boosting, gradient boosting machine, and gradient boosting machine (LightGBM). The proposed regressors are implemented based on the system's actual recorded dataset (consisting of 720 observations). The dataset consists of input variables, which are the wind speed (V), cloud cover, humidity, ambient temperature (T), solar radiation (SR), (T-io), (T-w), (T-v), and (T-t). Also, the output variable is represented by the productivity of the system. The dataset was separated into training (70%) and testing (30%) sets. In order to decrease regressors errors, hyperparameter optimization was employed. Gradientboosting approach provided the best prediction, with 95% R-2 accuracy and 39.57 root mean square error (RMSE) error. The LightGBM technique achieved 94% R-2 accuracy and 40.07 RMSE error in the testing dataset. The results reveal that Gradientboosting outperforms the cascaded forward neural network in predicting system productivity (CFNN).

关键词： Solar desalination Meteorological data boosting algorithms Modelling Productivity evaluation

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