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International Conference on Intelligent Computing and Information Science

作者： Wu, Peng Zhao, Hui Henan Univ Ctr Comp Kaifeng Peoples R China

ISBN: (纸本)9783642181283

The AdaBoost algorithm enables weak classifiers to enhance their performance by establishing the set of multiple classifiers, and since it automatically adapts to the error rate of the basic algorithm ill training through dynamic regulation of the weight of each sample, a wide range of concern has been aroused. This paper primarily makes some relevant introduction of Adaboost, and conducts an analysis and research of several aspects of the algorithm itself.

关键词： adaboost algorithm boosting algorithm analysis and research

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A Robust Intrusion Detection System using Ensemble Machine Learning 6

A Robust Intrusion Detection System using Ensemble Machine L...

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6th IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)

作者： Divakar, Subham Priyadarshini, Rojalina Mishra, Brojo Kishore Persistent Syst Pune Maharashtra India CV Raman Global Univ Bhubaneswar Odisha India GIET Univ Gunupur Odisha India

ISBN: (纸本)9781665419178

In today's world there is a rising need for network attack analysis because of rising cyber threats and attacks worldwide. Network traffic, if monitored dynamically in real-time could prevent a big cyber attack or even alert before. In this paper, UNSW-NB 15 dataset is used on Ensemble method to analyze the network traffic. The major contribution of this paper is the novel algorithms powered by boosting algorithm to come up with the best classifier from the list of classifiers. It compares the classifier in terms of accuracy as well as training time which is significant in real-time analysis of network traffic. We performed experiment in which we took 10 classifiers and our proposed algorithm came up with XGB Classifier as the best one from the set in terms of accuracy and training time combined. We have demonstrated the comparison between running time of complete experiment on Central Processing Unit (CPU) and Graphical Processing Unit GPU).

关键词： Intrusion Detection System Ensemble learning Security boosting algorithm

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Aggregated Channel Features with Optimum Parameters for Pedestrian Detection 7th

Aggregated Channel Features with Optimum Parameters for Pede...

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7th International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Bastian, Blossom Treesa Jiji, C. Victor Coll Engn Trivandrum Thiruvananthapuram Kerala India

ISBN: (纸本)9783319699004;9783319698991

Aggregated Channel Features (ACF) proposed by Dollar [3] provide strong framework for pedestrian detection. In this paper we show that, fine tuning the parameters of the baseline ACF detector can achieve competitive performance without additional channels and filtering actions. We experimentally determined the optimized values of four parameters of ACF detector: (1) size of training dataset, (2) sliding window stride, (3) sliding window size and (4) number of bootstrapping stages. Accordingly, our optimized detector using pre learned eigen filters achieved state of the art performance compared with other variants of ACF detector on Caltech pedestrian dataset.

关键词： Pedestrian detection ACF detector boosting algorithm

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A modified boosted support vector machine to rate banks

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BENCHMARKING-AN INTERNATIONAL JOURNAL 2021年第1期28卷 1-27页

作者： Viswanathan, Hari Hara Krishna Kumar Murugesan, Punniyamoorthy Rengasamy, Sundar Vilvanathan, Lavanya Natl Inst Technol Tiruchirappalli Dept Management Studies Tiruchirappalli India

Purpose The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks). Design/methodology/approach In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely "Modified Boosted Support Vector Machines" (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) andk-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM). Findings The performances of each algorithm have been compared based on one metric-the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques. Research limitations/implications This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model;the sole focus is on the result

关键词： Support vector machines boosting algorithm Back propagation neural networks Credit ratings Rating banks

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Recognizing Faces -- An Approach Based on Gabor Wavelets

Recognizing Faces -- An Approach Based on Gabor Wavelets

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作者： Shen, Linlin University of Nottingham

学位级别：博士

As a hot research topic over the last 25 years, face recognition still seems to be a difficult and largely problem. Distortions caused by variations in illumination, expression and pose are the main challenges to be dealt with by researchers in this field. Efficient recognition algorithms, robust against such distortions, are the main motivations of this research. Based on a detailed review on the background and wide applications of Gabor wavelet, this powerful and biologically driven mathematical tool is adopted to extract features for face recognition. The features contain important local frequency information and have been proven to be robust against commonly encountered distortions. To reduce the computation and memory cost caused by the large feature dimension, a novel boosting based algorithm is proposed and successfully applied to eliminate redundant features. The selected features are further enhanced by kernel subspace methods to handle the nonlinear face variations. The efficiency and robustness of the proposed algorithm is extensively tested using the ORL, FERET and BANCA databases. To normalize the scale and orientation of face images, a generalized symmetry measure based algorithm is proposed for automatic eye location. Without the requirement of a training process, the method is simple, fast and fully tested using thousands of images from the BioID and BANCA databases. An automatic user identification system, consisting of detection, recognition and user management modules, has been developed. The system can effectively detect faces from real video streams, identify them and retrieve corresponding user information from the application database. Different detection and recognition algorithms can also be easily integrated into the framework.

关键词： Face Recognition Gabor Wavelets Linear Subspace Methods Kernel Subspace Methods boosting algorithm Eye Location

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Comparative Evaluation of Weather Forecasting using Machine Learning Models 26

Comparative Evaluation of Weather Forecasting using Machine ...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Rahman, Md Saydur Tumpa, Farhana Akter Islam, Md Shazid Al Arabi, Abul Hossain, Md Sanzid Bin Ul Haque, Md Saad University of California Riverside United States Ahsanullah University of Science and Technology Bangladesh Texas A&M University CSE United States University of Central Florida United States University of Florida United States

ISBN: (纸本)9798350359015

Gaining a deeper understanding of weather and being able to predict its future conducts have always been considered important endeavors for the growth of our society. This research paper explores the advancements in understanding and predicting nature's behavior, particularly in the context of weather forecasting, through the application of machine learning algorithms. By leveraging the power of machine learning, data mining, and data analysis techniques, significant progress has been made in this field. This study focuses on analyzing the contributions of various machine learning algorithms in predicting precipitation and temperature patterns using a 20-year dataset from a single weather station in Dhaka city. algorithms such as Gradient boosting, Adaboosting, Artificial Neural Network, Stacking Random Forest, Stacking Neural Network, and Stacking KNN are evaluated and compared based on their performance metrics, including Confusion matrix measurements. The findings highlight remarkable achievements and provide valuable insights into their performances and features correlation. © 2023 IEEE.

关键词： boosting algorithm Machine Learning Precipitation Stacking Neural Network Temperature Weather Forecasting

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Predicting Initial Coin Offering Success Using Machine Learning and Human Capital Analysis 11

Predicting Initial Coin Offering Success Using Machine Learn...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Eljil, Khouloud Safi Nait-Abdesselam, Farid LIPADE EA 2517 Université Paris Cité Paris France

ISBN: (纸本)9798350367300

Initial Coin Offerings (ICOs) have emerged as a groundbreaking method for blockchain startups to raise capital, allowing projects to generate significant funding through the issuance of digital tokens. Despite their potential to raise millions, often outpacing traditional approaches like Initial Public Offerings (IPOs), ICOs remain high-risk investments due to limited regulatory oversight and a lack of standardization. This study investigates the impact of human capital on ICO success, employing machine learning techniques, including advanced boosting algorithms, to enhance predictive accuracy. Using data from 500 ICOs and analyzing over 5,524 LinkedIn profiles of project team members and advisors, we demonstrate that human capital factors can substantially inform success predictions. Our model achieves a notable 72% prediction accuracy, offering valuable insights amid the inherent volatility and unpredictability of ICO markets. These findings underscore the role of human capital in ICO outcomes, providing a data-driven framework for stakeholders to assess ICO prospects. © 2024 IEEE.

关键词： boosting algorithm Human capital ICO machine learning

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Enhanced Power Battery Fault Detection in New Energy Vehicles Using LightGBM

Enhanced Power Battery Fault Detection in New Energy Vehicle...

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2024 International Conference on New Power System and Power Electronics, NPSPE 2024

作者： Chang, Liang Lanzhou Vocational Technical College School of Automotive Engineering Lanzhou China

ISBN: (纸本)9798350389869

Under the escalating pressures of worldwide energy and environmental challenges, to uphold sustainable progression, there's a swift advancement in the sector of alternative fuel vehicles. As the main power source and one of the main fault causes of new energy vehicles, the nonlinear characteristics of power batteries make fault diagnosis more difficult. The efficiency of traditional battery fault diagnosis methods is low, and it cannot realize fault early warning. Therefore, improving the efficiency and accuracy of fault diagnosis of new energy vehicles has become a current research hotspot. In this paper, using real vehicle data, the random forest algorithm is used to analyze the importance of voltage, current and charge of battery systems. Three detection models based on lightweight gradient promotion (LightGBM), classification gradient promotion (CatBoost) and extreme gradient promotion (XGBoost) are established. Through comparison and analysis of model evaluation indicators such as AUC, accuracy rate, precision, F1 score, etc. The results show that LightGBM has the advantages of faster calculation speed, low fault false alarm rate and classification accuracy up to 99%, which solves the problems of low battery anomaly detection efficiency and low accuracy. The rapid fault location and analysis of the power battery of the new energy vehicle is realized, which has high application value. © 2024 IEEE.

关键词： big data boosting algorithm fault diagnosis LightGBM power battery

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Mit :: Lcs :: Tr :: Mit-Lcs-Tr-811

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2016年

Mit :: Lcs :: Tr :: Mit-Lcs-Tr-811 by published by

关键词： adaboost algorithm boosting boosting algorithm error generalization error hypothesis learning learning algorithm mit-lcs-tr-811 movies rankboost ranking ranking problem test error training error training set weak weak hypotheses weak hypothesis weak learner

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FACIAL LANDMARK LOCALIZATION VIA BOOSTED AND ADAPTIVE FILTERS

FACIAL LANDMARK LOCALIZATION VIA BOOSTED AND ADAPTIVE FILTER...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Zhou Lubing Wang Han Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781479923410

Recently, the ASEF and MOSSE filters have exhibited impressive performance in facial keypoint localization, which is often a vital step in facial image analysis. Correlation outputs of training samples are first designed as Gaussians, and the filters are reversely constructed via averaging and summed error minimization in Fourier domain, where correlation can be efficiently computed by element-wise multiplication. To further improve the performance, this paper proposes two kinds of techniques extended from ASEF and MOSSE: (1) add an error correction module, and increase the weights of inaccurately detected samples under the framework of boosting algorithm;(2) iteratively adjust the synthetic outputs to be more adaptive to image contents. Experimental results show the proposed methods are superior to ASEF and MOSSE in keypoint finding.

关键词： Correlation filters facial landmark localization boosting algorithm Fourier Transform

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