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A random feature mapping method based on the adaboost algorithm and results fusion for enhancing classification performance

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Shan, Wangweiyi Li, Dong Liu, Shulin Song, Mengmeng Xiao, Shungen Zhang, Hongli Changzhou Univ Sch Petr & Nat Gas Engn Changzhou 213164 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Ningde Normal Univ Sch Mech & Elect Engn Ningde 352100 Peoples R China Ningde Normal Univ Sch Informat Engn Ningde 352100 Peoples R China

The feature mapping method can improve data separability, enhance data representation ability, and reduce data processing complexity. However, on the one hand, the existing feature mapping methods have difficulty processing datasets of different dimensions and distributions adaptively, limiting the scope of application;on the other hand, a single feature mapping method has the problem of instability and poor generalization ability, weakening the classification ability of subsequent classifiers. This paper proposes a random feature mapping method based on the adaboost algorithm and results fusion to enhance classification performance. The method adopts horizontal expansion, fine-tuning weights through sparse autoencoders, and uses input-mapped features as feature nodes to generate multiple feature subsets for increasing stability. After training weak classifiers on each multiple feature subset, the weights of classifiers are adjusted adaptively by the adaboost ensemble algorithm. Finally, the method fuses weak classifiers twice to enhance classification performance, which abandons the traditional voting method and uses the weighted probability selection method. Experiments on twenty classic datasets show that the proposed method can effectively mine essential features and enhance classification accuracy compared with original datasets. For instance, the Balance dataset has an average classification accuracy of more than 20% higher than the original dataset on the KNN classifier. The proposed method outperforms alternative feature mapping methods in terms of performance and efficiency on different classifiers in most cases.

关键词： Random feature mapping Multiple feature subsets adaboost algorithm Results fusion Classification performance

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A method of badminton video motion recognition based on adaptive enhanced adaboost algorithm

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INTERNATIONAL JOURNAL OF BIOMETRICS 2025年第1-2期17卷 86-101页

作者： Chang, Yuntao Hunan City Univ Dept Phys Educ Yiyang 413000 Hunan Peoples R China

To overcome the problems of low recognition accuracy, poor recognition recall, and long recognition time in traditional badminton video action recognition methods, a badminton video action recognition method based on an adaptive enhanced adaboost algorithm is proposed. Firstly, the badminton video actions are collected through inertial sensors, and the badminton action videos are captured to construct an action dataset. The data in this dataset is normalised, and then the badminton video action features are extracted. The weighted fusion method is used to fuse the extracted badminton video action features. Finally, the fused action features are used as the basis, Construct a badminton video action classifier using the adaptive enhanced adaboost algorithm, and output the badminton video action recognition results through the classifier. The experimental results show that the proposed method has good performance in recognising badminton video actions.

关键词： inertial sensor weighted fusion method adaboost algorithm motion recognition data standardisation

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Intelligent Island detection method of DC microgrid based on adaboost algorithm

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ENERGY REPORTS 2023年 9卷 970-982页

作者： Zhi, Na An, Yawei Zhao, Yan Qiu, Jilin Xian Univ Technol Xian 710048 Shaanxi Peoples R China

Aiming at the problems of large detection blind areas and difficult threshold settings in traditional passive islanding detection methods, this paper proposes an intelligent passive islanding detection method based on the adaptive boosting (adaboost) algorithm. This method first uses feature screening technology to obtain the key feature electrical quantities of DC microgrid islands and form a sample set, and then rely on the adaboost algorithm to generate a high-precision island classification model to realize the detection and classification of DC microgrid operation status. It has the advantages of automatic setting threshold, small detection blind area, and high accuracy. In addition, aiming at the problem that the classification results of the intelligent islanding detection algorithm based on machine learning cannot be directly used as the islanding discrimination results, an islanding criterion method based on the sliding window algorithm is added on the basis of the classification algorithm. This method realizes the accurate detection of the DC microgrid islanding operation state by the secondary analysis and judgment of the classification results of the islanding classification model. Finally, a DC microgrid model is built to simulate and verify the proposed method. The results show that the method can quickly and accurately complete the islanding detection of the DC microgrid.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： DC microgrid Islanding detection adaboost algorithm Island discrimination

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Yarn Quality Prediction for Small Samples Based on adaboost algorithm

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Journal of Donghua University(English Edition) 2023年第3期40卷 261-266页

作者：刘智玉陈南梁汪军 College of Textiles Donghua UniversityShanghai 201620China Key Laboratory of Textile Science&Technology Ministry of EducationDonghua UniversityShanghai 201620China

In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an adaboost algorithm(adaboost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for *** prediction experiments of the yarn evenness and the yarn strength were *** coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these *** the prediction experiments,the determination coefficient of the yarn evenness prediction result of the adaboost model is 76% and 87% higher than that of the LR model and the MLP model,*** determination coefficient of the yarn strength prediction result of the adaboost model is slightly higher than that of the other two *** that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the adaboost model has the best adaptability for the nonlinear dataset among the three *** addition,the adaboost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample *** is proved that the adaboost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.

关键词： stability and generalization ability for small samples.Key words:yarn quality prediction adaboost algorithm small sample generalization ability

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Model Reference Adaptive Control of Semi-active Suspension Model Based on adaboost algorithm for Rollover Prediction

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SAE INTERNATIONAL JOURNAL OF VEHICLE DYNAMICS STABILITY AND NVH 2022年第1期6卷 71-86页

作者： Zhu Tianjun Wan, Hegao Wang, Zhenfeng Wei, Ma Xu, Xuejiao Zou Zhiliang Du Sanmiao Hebei Univ Engn Coll Mech & Equipment Engn Handan Peoples R China Zhaoqing Univ Dept Mech & Automot Engn Zhaoqing Peoples R China CATARC Tianjin Automot Engn Res Inst Co Ltd Tianjin Peoples R China JA SOLAR Xingtai Co Ltd Xingtai Peoples R China Nanchang Univ Sch Econ & Management Nanchang Jiangxi Peoples R China

Due to their large volume structure, when a heavy vehicle encounters sudden road conditions, emergency turns, or lane changes, it is very easy for vehicle rollover accidents to occur;however, well-designed suspension systems can greatly reduce vehicle rollover occurrence. In this article, a novel semi-active suspension adaptive control based on adaboost algorithm is proposed to effectively improve the vehicle rollover stability under dangerous working conditions. This research first established a vehicle rollover warning model based on the adaboost algorithm. Meanwhile, the approximate skyhook damping suspension model is established as the reference model of the semi-active suspension. Furthermore, the model reference adaptive control (MRAC) system is established based on Lyapunov stability theory, and the adaptive controller is designed. Finally, on the same road condition, the rollover warning control simulations are carried out under the following conditions: the 180-degree step, the fishhook, and the double-lane-change condition. Simulation results show that the proposed reference adaptive control based on the adaboost algorithm for rollover control can effectively predict vehicle rollover in early warning and improve the anti-rollover capability of vehicles.

关键词： adaboost algorithm Warning rollover Semi-active suspension Model reference adaptive control

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Application of adaboost algorithm in Enterprise Financial Risk Analysis Model 1

Application of Adaboost Algorithm in Enterprise Financial Ri...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Zhang, Donghong Li, Hanjun Wenhua College Department of Accounting Department of Financial Management Hubei Wuhan China

ISBN: (纸本)9798350316469

adaboost (Adaptive Boosting) algorithm is an ensemble learning method, which combines multiple weak classifiers to build a strong classifier, and improves the performance of the model iteratively. It is widely used in various fields, including enterprise financial risk analysis models. Therefore, this article used the adaboost algorithm to study the financial risk analysis model, with the aim of improving the accuracy of risk prediction. This paper mainly used the method of experiment and algorithm comparison to compare the performance of adaboost algorithm, decision tree algorithm and Logistic regression model. The experimental data showed that the recall rate of high-risk categories was 0.91, which means that the success rate of the adaboost model in predicting high-risk companies was 91%;the specificity was 0.902, which was 90.2% of the predicted risk-free enterprises in the model. The F1 value reached 0.818, indicating that the model performs well in predicting risk categories. © 2023 IEEE.

关键词： adaboost algorithm Analysis Model Enterprise Finance Risk Factor

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Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-adaboost algorithm: A Case Study From Jiaozhou Bay, China

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IEEE JOURNAL OF OCEANIC ENGINEERING 2021年第2期46卷 509-519页

作者： Ji, Xue Yang, Bisheng Tang, Qiuhua Key State Key Lab Informat Engn Surveying Mapping Wuhan 430079 Hubei Peoples R China MNR Inst Oceanog 1 Qingdao 266061 Peoples R China

When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (adaboost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines adaboost with the particle swarm optimization (PSO). The PSO-BP-adaboost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an adaboost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the adaboost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-adaboost algorithm. The PSO-BP-adaboost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-adaboost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.

关键词： Backscatter Sediments Acoustics Discrete wavelet transforms Sonar Microscopy Neural networks Acoustic seabed classification adaboost algorithm back-propagation neural network (BPNN) multibeam echosounder particle swarm optimization (PSO)

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Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive adaboost algorithm

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10098页

作者： Zhang, Feng Qiao, Zijie Tian, Yuxiang Wu, Mingying Xu, Xiayu Northwestern Polytech Univ Sch Mech Civil Engn & Architecture Xian 710129 Peoples R China

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive adaboost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the adaboost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method's iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

关键词： adaboost algorithm adaptive iteration complex structure multiple-failure mode failure probability

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Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With adaboost algorithm

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IEEE ACCESS 2021年 9卷 94040-94052页

作者： An, Guoqing Jiang, Ziyao Cao, Xin Liang, Yufei Zhao, Yuyang Li, Zheng Dong, Weichao Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China Hebei Engn Lab Wind Power Photovolta Coupling Hyd Shijiazhuang 050018 Hebei Peoples R China Hebei Construct & Investment Grp New Energy Co Lt Shijiazhuang 050051 Hebei Peoples R China

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

关键词： adaboost algorithm extreme learning machine optimization algorithm wind power prediction

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Research on facial feature-based gender intelligent recognition based on the adaboost algorithm

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INTERNATIONAL JOURNAL OF BIOMETRICS 2021年第1期13卷 40-50页

作者： Wang, Jing Henan Univ Anim Husb & Econ Dept Energy & Intelligence Engn Zhengzhou 450011 Peoples R China

In order to overcome the problem of poor facial recognition intelligence and weak gender judgment, a new method based on adaboost algorithm for facial feature-based gender intelligence recognition is proposed in this paper. In this method, the three-dimensional special point detection, weak perspective projection, spatial region segmentation and other methods are employed to construct the facial feature information sampling model. The adaboost algorithm is used to analyse the matching between facial features and gender, on which facial-feature gender intelligent recognition is performed according to the distribution of the eyes, nose and mouth of the face image, and the edge contour detection model of the face image is constructed. The experimental results show that the method has the advantages of good intelligence, high recognition precision and short time cost in face-based gender recognition.

关键词： adaboost algorithm facial features intelligent identification gender

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