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A NOTE ON IMPLEMENTATIONS OF THE boosting algorithm AND HETEROGENEOUS MULTISCALE METHODS

SIAM JOURNAL ON NUMERICAL ANALYSIS 2015年第5期53卷 2472-2487页

作者： Maclean, John Univ Sydney Sch Math & Stat Sydney NSW 2006 Australia

We present improved convergence results for the boosting algorithm (BA) and demonstrate that an existing formulation of the heterogeneous multiscale methods (HMM) is accurate to first order only in the macro time step, regardless of the order of the numerical solvers employed. These results are obtained by considering the BA and two other formulations of HMM as special cases of a general formulation of HMM applied to dissipative stiff ordinary differential equations.

关键词： multiscale integrators heterogeneous multiscale methods boosting algorithm

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Improving OCR performance using character degradation models and boosting algorithm

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PATTERN RECOGNITION LETTERS 1997年第11-13期18卷 1415-1419页

作者： Mao, JC Mohiuddin, KM IBM Corp Almaden Res Ctr San Jose CA 95120 USA

We introduce three character degradation models in a boosting algorithm for training an ensemble of character classifiers. We also compare the boosting ensemble with the standard ensemble of networks trained independently with character degradation models. An interesting discovery in our comparison is that although the boosting ensemble is slightly more accurate than the standard ensemble at zero reject rate, the advantage of the boosting training over independent training quickly disappears as more patterns are rejected. Eventually the standard ensemble outperforms the boosting ensemble at high reject rates, Explanation of such a phenomenon is provided in the paper. (C) 1997 Elsevier Science B.V.

关键词： combining multiple classifiers boosting algorithm neural network ensemble OCR character degradation

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Data classification with binary response through the boosting algorithm and logistic regression

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 69卷 62-73页

作者： de Menezes, Fortunato S. Liska, Gilberto R. Cirillo, Marcelo A. Vivanco, Mario J. F. Fed Univ Lavras UFLA Dept Phys DFI POB 3037 BR-37200000 Lavras MG Brazil Fed Univ Lavras UFLA Dept Stat DES POB 3037 BR-37200000 Lavras MG Brazil

The task of classifying is natural to humans, but there are situations in which a person is not best suited to perform this function, which creates the need for automatic methods of classification. Traditional methods, such as logistic regression, are commonly used in this type of situation, but they lack robustness and accuracy. These methods do not not work very well when the data or when there is noise in the data, situations that are common in expert and intelligent systems. Due to the importance and the increasing complexity of problems of this type, there is a need for methods that provide greater accuracy and interpretability of the results. Among these methods, is boosting, which operates sequentially by applying a classification algorithm to reweighted versions of the training data set. It was recently shown that boosting may also be viewed as a method for functional estimation. The purpose of the present study was to compare the logistic regressions estimated by the maximum likelihood model (LRMML) and the logistic regression model estimated using the boosting algorithm, specifically the Binomial boosting algorithm (LRMBB), and to select the model with the better fit and discrimination capacity in the situation of presence(absence) of a given property (in this case, binary classification). To illustrate this situation, the example used was to classify the presence (absence) of coronary heart disease (CHD) as a function of various biological variables collected from patients. It is shown in the simulations results based on the strength of the indications that the LRMBB model is more appropriate than the LRMML model for the adjustment of data sets with several covariables and noisy data. The following sections report lower values of the information criteria AIC and BIC for the LRMBB model and that the Hosmer-Lemeshow test exhibits no evidence of a bad fit for the LRMBB model. The LRMBB model also presented a higher AUC, sensitivity, specificity and accuracy

关键词： boosting algorithm Data classification Logistic regression Information criteria AIC BIC Selection of models Monte Carlo Simulation

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Design of Nonlinear PID Control Systems Using boosting algorithm

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2011年第9期94卷 52-58页

作者： Kawanari, Sho Ohnishi, Yoshihiro Kure Coll Technol Kure Japan

There are many nonlinear systems among process control systems. However, it is difficult to handle nonlinear properties with a linear controller. Thus, various methods have been studied in this field. On the other hand, a scheme called boosting has been proposed in the machine learning field. This scheme can obtain highly accurate conditions by combining less-accurate conditions. boosting can obtain better results than a neural network with fewer learning data. In this paper, we propose a method for the design of nonlinear PID control systems using the boosting algorithm. The original boosting is capable of dealing only with two-valued variables, and thus we extend the algorithm for function approximation using it. Finally, simulation examples are presented in order to demonstrate the effectiveness of the proposed scheme. (C) 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94( 9): 52-58, 2011;Published online in Wiley Online Library (***). DOI 10.1002/ecj.10310

关键词： boosting algorithm AdaBoost PID control

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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data

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Energy Informatics 2024年第1期7卷 1-25页

作者： Wang, Jiali Chen, Jia School of Intelligent Manufacturing and Information Engineering Shanghai Institute of Commerce & Foreign Languages Shanghai201399 China Co. Ltd Jinhua321200 China

In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. boosting is a machine learning technique that combines multiple weak learners into a strong learner. Big data refers to large-scale, complex datasets that exceed traditional data processing capabilities. Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved boosting algorithms were proposed, namely Light Gradient boosting Machine (LightGBM), eXtreme Gradient boosting Tree (XGBoost), and Gradient boosting Decision Tree (CatBoost). The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. The accuracy values of XGBoost, CatBoost, and LightGBM are 97.84%, 98.57%, and 99.16%, respectively. The recall rates of XGBoost, CatBoost, and LightGBM models are all 1. The F1 values of GBoost, CatBoost, and LightGBM are 0.873, 0.983, and 0.985, respectively. The power battery is the core component of new energy vehicles, and its safety performance directly affects the operational safety of the vehicle. Timely identification and diagnosis of battery faults can effectively reduce potential accidents such as battery overheating and short circuits. Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this method can effectively reduce the risk of serious safety accidents and improve the overall operational reliability of new energy vehicles during driving. © The Author(s) 2024.

关键词： boosting algorithm Fault diagnosis New energy vehicles Power battery RF

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Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2017年第1期5卷 126-133页

作者： Jiang, Yu Chen, Xingying Yu, Kun Liao, Yingchen Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China

Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: (1)proposing a novel hybrid method using boosting algorithm and a multi-step forecast approach to improve the forecasting capacity of traditional ARMA model;(2)calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.

关键词： Hybrid method Multi-step-ahead prediction Wind power forecast boosting algorithm Time series model

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Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG

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EPILEPSY & BEHAVIOR 2015年 45卷 8-14页

作者： Yan, Aiyu Zhou, Weidong Yuan, Qi Yuan, Shasha Wu, Qi Zhao, Xiuhe Wang, Jiwen Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Shandong Univ Suzhou Inst Suzhou 215123 Peoples R China Shandong Univ Qilu Hosp Jinan 250100 Peoples R China

Automatic detection of seizures has vital significance for epileptic diagnosis and can efficiently reduce the workload of the medical staff. In this study, a novel seizure detection method based on Stockwell transform is proposed for intracranial long-term EEG data. The Stockwell transform is employed to obtain the time-frequency representation of the EEG signals, and then the power spectral density is calculated in the time-frequency plane to characterize the behavior of EEG recordings. After that, a classifier based on gradient boosting algorithm is used to make the classification. Finally, the postprocessing is utilized on the outputs of the classifier to obtain more stable and accurate detection results, which includes Kalman filter, threshold judgment, and collar technique. The performance of this method is assessed on the publicly available EEG database which contains approximately 533 h of intracranial EEG recordings. The experimental results indicate that the proposed method can achieve a satisfactory sensitivity of 94.26%, a specificity of 96.34%, as well as a very short delay time of 0.56 s. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Electroencephalogram (EEG) Stockwell transform Seizure detection Power spectral density boosting algorithm

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Spam classification using adaptive boosting algorithm

Spam classification using adaptive boosting algorithm

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6th IEEE/ACIS International Conference on Computer and Information Science in conjunction with 1st IEEE/ACIS International Workshop on e-Activity

作者： Ali, Abm Shawkat Xiang, Yang Univ Cent Queensland Sch Comp Sci Rockhampton Qld 4702 Australia Univ Cent Queensland Sch Management & Informat Syst Rockhampton Qld 4702 Australia

ISBN: (纸本)9780769528410

Spam is no doubt a new and growing threat to the Internet and its end users. This paper investigates current approaches for blocking spam and proposes a new spam classification method by using adaptive boosting algorithm. Experiment is carried out to evaluate the results of spam filtering. We find adaptive boosting algorithm is an effective approach. to solve the spam problem. We also find that default method in WEKA such as DecisionStump is not actually the best associated algorithm to filter spam. After comparing DecisionStump, J48, and NaiveBayes we conclude J48 is the most suitable associated algorithm to filter spam with high, true positive rate, low false positive rate and low computation time.

关键词： spam filtering boosting algorithm

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An Intelligent Intrusion Detection Scheme Powered by boosting algorithm 11

An Intelligent Intrusion Detection Scheme Powered by Boostin...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： SubhamDivakar Priyadarshini, Rojalina Barik, Rabindra Kumar Roy, Diptendu Sinha Persistent Syst Pune Maharashtra India CV Raman Global Univ Bhubaneswar Odisha India KIIT Deemed Univ Bhubaneswar Odisha India Natl Inst Technol Shillong Meghalaya India

ISBN: (纸本)9781665414517

Security is always a major concern in today's world. Due to the prevalent techniques like Internet of Things (IoT), Fog/Edge computing and the vast use of social networking, there is a significant increase in the generation of network traffic data. For this reason, proper and fast mechanisms are needed to monitorvariety of data to fight against the vulnerabilities and threats those may occur in the system. In the present article, a machine learning based Intrusion Detection Scheme (IDS) is being proposed. This system can monitor and analyze the incoming network traffic whether is normal. UNSW-NB 15 dataset is used to validate the machine learning model which is powered by boosting *** of the boosting algorithms such as Adaptive boosting (AdaBoost), Extreme Gradient boosting (XGBoost) and Gradient boosting Classifier (GBC) are trained over the six baseline models such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), two variants of Random Forest Model, Gaussian Naive Bayes (GNB). The performance of the IDS is measured in terms of correct analysis of the network traffic as normal or abnormal and the time taken to detect it. As per the observed results the proposed IDS system is providing the best results for XGB model which gives 95.57 % of accuracy and the time taken to do it is come out as 3.03 seconds. The entire experiment is executed both in Central Processing Unit (CPU) and Graphical Processing UnitGPU) environment and a comparative analysis is done in terms of execution time.

关键词： Intrusion Detection System Security boosting algorithm

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Shared bicycle dynamic distribution model based on boosting algorithm 31

Shared bicycle dynamic distribution model based on Boosting ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Han, Lu Yu, Chongchong Chen, Yunbing Tang, Xiaojiang Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9781728101057

The number of shared bicycles is large and the parking is not standardized. Accurate position prediction of the bicycle can effectively prevent the collision between the number of vehicles and the parking position. The traditional machine learning algorithm is slowly to process and can not meet the accuracy requirements. Therefore, the boosting algorithm which promote the weak learner to a strong learner is adopted to enhance running speed and accuracy. At the same time, the way of constructing feature groups is proposed, and the importance of features is evaluated. The comparison experiment compares the prediction capabilities of the three boosting algorithms, XGboost, LightGBM and Catboost. The example verification shows that the LightGBM algorithm has the highest test accuracy. and then this algorithm is used to predict the dynamic distribution of the Mobike in Beijing throughout the day. And show the heat distribution map of the prediction results of Mobike in different time periods.

关键词： Shared bicycle boosting algorithm Feature group Position prediction Dynamic distribution

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