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6th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Burduk, Robert Wroclaw Univ Technol Dept Syst & Comp Networks PL-50370 Wroclaw Poland

ISBN: (纸本)9783319054582;9783319054575

This paper presents the adaboost algorithm that provides for the imprecision in the calculation of weights. In our approach the obtained values of weights are changed within a certain range of values. This range represents the uncertainty of the calculation of the weight of each element of the learning set. In our study we use the boosting by the reweighting method where each weak classifier is based on the recursive partitioning method. A number of experiments have been carried out on eight data sets available in the UCI repository and on two randomly generated data sets. The obtained results are compared with the original adaboost algorithm using appropriate statistical tests.

关键词： adaboost algorithm weight of the observation machine learning

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Driver's Eyes State Detection Based on adaboost algorithm and Image Complexity 6

Driver's Eyes State Detection Based on Adaboost Algorithm an...

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6th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Li, Xue Wu, QingXiang Kou, Yu Hou, Lei Xie, Haihui Fujian Normal Univ Coll Photon & Elect Engn Minist Educ Key Lab OptoElect Sci & Technol Med Fuzhou Fujian Peoples R China

ISBN: (纸本)9781467393935

Eyes state detection is a very important issue in the driver's fatigue detection. In this paper, a novel method is proposed to solve the difficulties and shortcomings in the eyes state detection. Gray-scale transformation and median filtering are used to preprocess images. And then, adaboost algorithm is used to train the cascade strong classifiers based on the characteristics of Haar to extract the face. In order to reduce the complexity of eyes location algorithm, the only upper part of face is sheared and an image complexity-based algorithm is proposed to locate eyes precisely and solve difficulties to detect the eyes when it closed in current methods. In the proposed algorithm image complexity and maximum connected components are used to locate precise positions of the eyes and judge the state of eyes. The experimental results show that the algorithm has improved the eyes state detection accuracy and has high robustness.

关键词： Fatigue Detection adaboost algorithm Image Complexity The Maximum Connected Components

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Designing and Optimizing the Method for Pedestrian Detection Based on adaboost algorithm 11

Designing and Optimizing the Method for Pedestrian Detection...

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11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Su, Yunpeng Fan, Binwen Yang, Qiliang Shenzhen Grad Sch Harbin Inst Technol Key Lab IOT Terminal Pivotal Technol Shenzhen Peoples R China

ISBN: (纸本)9781479951482

The designing based on adaboost algorithm not only achieved the nighttime pedestrian detection module of auxiliary driving system, but also realized the system optimization on issues of low detection speed and precision. In this design, variable step length and partition scanning track methods are used to improve the detection speed and variance normalization approach was applied to eliminate the influences to the detection result caused by light factors, moreover, multi-scale fusion technology was utilized to make analysis to the detected rectangular box, in this case, the redundant portion of detection result can be removed, and thus improved the detection rate and reduced the false alarm rate.

关键词： Pedestrian Detection adaboost algorithm multi-scale fusion

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A kind of improved adaboost algorithm 7

A kind of improved AdaBoost algorithm

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7th International Conference Intelligent Computation Technology Automation (ICICTA)

作者： Liao Shaowen Yong, Chen Hexi Univ Informat Technol & Media Inst Zhangye 734000 Gansu Peoples R China Artillery Training Base Gen Staff XuanHua Xuanhua 075100 Hebei Peoples R China

ISBN: (纸本)9781479966363

adaboost algorithm is a kind of very important feature classification machine learning algorithm, But if difficult samples exist in the training samples, with the iterative Nurnber increasing, this easily leads to degeneration Phenomenon, and reduces the generalization ability of the classifier. In view of the face detection under complex background degeneration appeared problem, This article Proposes LWE-adaboost algorithm which can limit weight expansion, the experimental results indicate that the LWE-adaboost algorithm can restrain the recurrence of degeneration Phenomenon well.

关键词： Face detection adaboost algorithm Integral image Weak classifier Strong classifier Cascaded classifier

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Power System Transient Stability Prediction Based on Sample Pre-screening of SVM algorithm and adaboost algorithm 5

Power System Transient Stability Prediction Based on Sample ...

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5th International Conference on Power and Energy Engineering (ICPEE)

作者： Chen, Xingyun Nie, Kejian State Grid Fujian Econ Res Inst Fuzhou Peoples R China

ISBN: (纸本)9781665420464

As the stability problem of modern power systems is prominent, the stable operation of the power system is more and more popular in power system analysis. To solve the problem of imbalance between the number of unstable and stable samples in transient stability prediction for power system transient prediction using machine learning methods, the paper proposes a transient stability prediction method for power system based on the SVM ( Support Vector Regression) algorithm sample pre-screening and adaboost (Adaptive Boosting) algorithm. The SVM algorithm is used to partition the sample space of the training sample set, obtain the hyperplane for partitioning, calculate the distance of each stable sample to the hyperplane and rank them. Stable samples with the same number of unstable samples are selected at certain intervals to form a new training sample set, and the adaboost algorithm is used to perform transient stability prediction of the power system. It is demonstrated in the IEEE39 system that the proposed method is more accurate than general machine learning algorithms and commonly used methods to deal with sample data imbalance.

关键词： power system transient stability machine learning SVM algorithm adaboost algorithm

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Miner Face Detection is Based on Improved adaboost algorithm

Miner Face Detection is Based on Improved AdaBoost Algorithm

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Jiang, Chao Han, Gu-yong Tian, Lei Lu, Song Huang, Wei-xing Air Force Serv Coll Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781467329644;9781467329637

This article connects with Coal mine video monitoring image be impacted for special environment, which be vulnerable to mineral dust in coal mines, light, as well as miner's safety helmet for the realization of face detection in real-time and accuracy, I will study on face identification and analysis on the characters of behavior in the follow-up work for getting a good foundation, which will be in intelligent Coal mine video monitoring. This article simulates rectangle Haar-like character and Extended Haar-like character of the adaboost algorithm about face detection in real-time and accuracy, is based on OpenCV, also describes briefly the rectangular Haar-like characteristic model and about computational algorithm and faster algorithm of the characteristic value, analysis detailedly extended Haar-like character model and the characteristic value of computational algorithm-integral image. Experimental resulted show that extended Haar-like characteristic model can be implemented more quickly and more accurately in the miners' face detection, as well as real-time.

关键词： adaboost algorithm Face detection Machine vision Monitoring image

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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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The Research of Face Detection Method Based on adaboost algorithm and Skin Color Segmentation

The Research of Face Detection Method Based on Adaboost Algo...

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IEEE International Conference on Imaging Systems and Techniques (IST) / IEEE International School on Imaging

作者： Liu Chang Beihang Univ Sch Instrumentat Sci & Optoelect Engn Dept Measurement Control & Informat Technol Beijing 100191 Peoples R China

ISBN: (纸本)9781538616208

As a key pre-processing of face recognition, face detection is an important foundation of some intelligent applications such as video retrieval and human-computer interaction. The performance of the adaboost algorithm is remarkable in the face detection methods, but there are still gaps. Concerning the high false positive rate of adaboost algorithm, the skin color segmentation method based on Gaussian model is introduced, which is used as the front and back processing of adaboost algorithm, respectively. Experiments show that the using of these two methods can significantly reduce the false positive rate in the detection of real-time video stream, while the skin color segmentation as a pre-processing can increase the detection speed by more than 20%, which can help achieve a stable and efficient real-time face detection system.

关键词： Face detection adaboost algorithm skin color segmentation real-time video streaming

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Brillouin Frequency Shift Extraction Based on adaboost algorithm

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SENSORS 2022年第9期22卷 3354-3354页

作者： Zheng, Huan Xiao, Feng Sun, Shijie Qin, Yali Zhejiang Univ Technol Inst Fiber Opt Commun & Informat Engn Coll Informat Engn Hangzhou 310023 Peoples R China

The Brillouin Optical Time-Domain Analyzer assisted by the adaboost algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the adaboost algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.

关键词： distributed fiber sensing Brillouin scattering Brillouin frequency shift adaboost algorithm

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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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