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Stratification of High-Risk Hypertensive Patients Using Hybrid Heart Rate Variability Features and boosting algorithms

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IEEE ACCESS 2021年 9卷 62665-62675页

作者： Deka, Dipen Deka, Bhabesh Cent Inst Technol CIT Kokrajhar Dept Instrumentat Engn Kokrajhar 783370 India Tezpur Univ Sch Engn Dept Elect & Commun Engn ECE Tezpur 784028 Assam India

Hypertension is a global challenge to the public health which can easily lead to life-threatening vascular diseases unless control measures are adopted. Considering the prevalence of vascular diseases and their fatality, early detection of high-risk patients is an important problem in the present world. Heart rate variability (HRV) analysis can be an effective prognostic tool to identify the characteristics of vulnerable patients, considering its reliability in predicting sudden cardiac deaths. However, challenge lies in identifying tenuous differences in HRV between the low-risk and high-risk patients at the early stage. With this motivation, we propose a hybrid approach based on dual-tree complex wavelet packet transform (DTCWPT) and linear time domain as well as nonlinear analysis of HRV signal to extract multitudinous features. A key issue before the HRV analysis of such patients is the presence of marked amount of ectopic beats, which is addressed by using time-varying auto regressive (TVAR) technique. The features extracted from TVAR edited HRV signals are shortlisted by minimum redundancy maximum relevance algorithm for an efficient classifier modeling. Furthermore in this study, we propose to use cost-sensitive RUSBoost (CS-RUSBoost) algorithm for handling the class imbalance problem of the data. A comparative performance evaluation of CS-RUSBoost with RUSBoost, SMOTEBoost, asymmetric AdaBoost algorithm shows a superior result by CS-RUSBoost with G-mean of 0.9352 and F1 score of 0.9347.

关键词： Heart rate variability Electrocardiography Hypertension Feature extraction Diseases Support vector machines Time-domain analysis Heart rate variability hypertension dual-tree complex wavelet packet transform class imbalance boosting algorithm

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An efficient intrusion detection system using a boosting-based learning algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2006年第4期27卷 223-231页

作者： Yu, Zhenwei Tsai, Jeffrey J. P. Univ Illinois Dept Comp Sci 851 S Morgan StRoom 1120SEO Chicago IL 60607 USA

boosting is effective in improving the accuracy of a learner. In this paper, we present our research in developing a Multi-Class SLIPPER (MC-SLIPPER) system for intrusion detection from a boosting-based learning algorithm. Our system is built from multiple available binary SLIPPER modules. Multiple prediction-confidence based strategies are proposed and applied to arbitrate the final prediction among predictions from all binary SLIPPER modules. Our MC-SLIPPER system is evaluated on the KDDCUP'99 intrusion detection dataset. The experimental results show that the system achieves the best performance using the BP neural network. And the system using other prediction strategies gets better performance than the winner of the KDDCUP'99 contest does in term of misclassification cost.

关键词： BP neural network boosting algorithm intrusion detection KDDCUP'99 contest misclassification cost prediction confidence

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Study on the influence of surrounding urbanSO2,NO2, andCOon haze formation in Beijing based onMF-DCCAand boosting algorithms

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第24期32卷 e5921-e5921页

作者： Zhang, Xin-xin Gu, Lei-lei Chen, Hong Jia, Guo-zhu Sichuan Normal Univ Coll Phys & Elect Engn Chengdu 610101 Peoples R China

The formation of haze depends on the complex evolution behavior of SO2, NO2, and CO. We explore the influence of surrounding urban oxides on haze formation in Beijing. From the perspective of time evolution, multifractal detrended cross-correlation analysis (MF-DCCA) method quantitatively shows that all oxides reveal persistent cross-correlations (h(xy): 0.640 sim;0.995) in the short term and long term. The correlation characteristics of the same oxide in different regions are compared. In the short term, Zhangjiakou (SO2/NO2) and Tianjin (CO) have the strongest multifractal features, while Zhangjiakou (SO2/NO2/CO) is more prominent in the long term. Their sources are mainly Fat-tailed. Ignoring the characteristics of timing, the fitting degree (R-2) of LightGBM model to PM2.5 concentration regression is 0.862 after the addition of neighboring oxides, an increase of 2.0%. CatBoost'sR(2)improves by 6.4%. The feature importance score indicates that Langfang's SO(2)and Chengde's CO contribute the most to the formation of PM2.5. This study has certain reference value for the formulation of oxide emission strategy in the surrounding area of Beijing.

关键词： Beijing China boosting algorithm MF-DCCA oxides PM2 5

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Imbalanced Data Classification algorithm Based on boosting and Cascade Model

Imbalanced Data Classification Algorithm Based on Boosting a...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Zhang, Xiaolong Cheng, Chao Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China

ISBN: (纸本)9781467317146

Traditional classification algorithms are difficult in dealing with imbalance data. This paper proposes a classification algorithm called CascadeBoost, which combines with the advantages of boosting algorithm and cascade model that can learn imbalance data. Cascade model allows the pre-training data to be balanced by gradually reducing the number of the major class;and then the most rich information samples based on the weight distribution can be gradually selected using boosting algorithm. The experimental results show that the proposed method obtains better performance compared to other methods.

关键词： Imbalance Data boosting algorithm Cascade Model SVM AUC

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Performance Investigation of Different boosting algorithms in Predicting Chronic Kidney Disease 2

Performance Investigation of Different Boosting Algorithms i...

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2nd International Conference on Sustainable Technologies for Industry 4.0 (STI)

作者： Nishat, Mirza Muntasir Faisal, Fahim Dip, Rezuanur Rahman Shikder, Md Fahim Ahsan, Ragib Asif, Md Asfi-Ar-Raihan Udoy, Mahmudul Hasan Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781665404891

This paper implies an investigative approach to study the performance of different boosting algorithms in predicting chronic kidney disease (CKD) more accurately. In recent years, CKD has reached a global prevalence with much severity which can lead to end-stage renal disease (ESRD) if not detected early. Various boosting machine learning algorithms have been proven to be an effective tool to detect CKD in its initial stages. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier containing 25 attributes. However, four different data frames were constructed by four different strategies (mean, median, mode, and null dropping method) to facilitate the missing values in the dataset. Eventually, three boosting algorithms were studied and corresponding confusion matrices are portrayed. Hence, a broad comparative investigation was conducted in terms of accuracy, precision, sensitivity, F1 score, ROC-AUC of each algorithm. Maximum accuracy of 99.75% was observed in the case of AdaBoost and LightGBM algorithms while 99.5% accuracy was noticed for XGBoost.

关键词： boosting algorithm Chronic Kidney Disease (CKD) UCI dataset Accuracy

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Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data

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COMPUTERS IN INDUSTRY 2023年 153卷

作者： Kim, Yoonseok Lee, Taeheon Hyun, Youngjoo Coatanea, Eric Mika, Siren Mo, Jeonghoon Yoo, Youngjun Korea Inst Ind Tech KITECH Ansan South Korea Yonsei Univ Dept Informat & Ind Engn Seoul South Korea Tampere Univ Mfg Engn & Syst Engn Tampere Finland VTT Tech Res Ctr Finland Helsinki Finland Korea Inst Ind Tech KITECH 89 Yangdaegiro GilIpjang Myeon Cheonan Si 31056 Chungcheongnam South Korea Engn Bldg D Rm 1004 50 Yonsei Ro Seoul 03722 South Korea

This study proposes a methodology for detecting anomalies in the manufacturing industry using a self-supervised representation learning approach based on deep generative models. The challenge arises from the limited availability of data on defective products compared with normal data, leading to degradation in the performance of deep learning models owing to data imbalances. To address this limitation, we propose a process that leverages the Gramian angular field to transform time-series data into images, applies StyleGAN for image augmentation of anomalous data, and utilizes a boosting algorithm for classifier selection in supervised learning. Additionally, we compared the accuracy of the classifier before and after data augmentation. In experimental cases involving CNC milling machine data and wire arc additive manufacturing data, the proposed approach outperformed the approach before augmentation, resulting in improved precision, recall, and F1-score for anomaly detection. Furthermore, Bayesian optimization of the hyperparameters of the boosting algorithm further enhanced the performance metrics. The proposed process effectively addresses the data imbalance problem, and demonstrates its applicability to various manufacturing industries.

关键词： Anomaly detection Time-series data Generative adversarial network boosting algorithm

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Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India

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URBAN CLIMATE 2023年 49卷

作者： Saravanan, Subbarayan Abijith, Devanantham Reddy, Nagireddy Masthan Parthasarathy, K. S. S. Janardhanam, Niraimathi Sathiyamurthi, Subbarayan Sivakumar, Vivek Natl Inst Technol Dept Civil Engn Trichy Tamil Nadu India Natl Inst Technol Karnataka Dept Water Resources & Ocean Engn Surathkal India Annamalai Univ Fac Agr Dept Soil Sci & Agr Chem Annamalainagar Tamilnadu India Hindusthan Coll Engn & Technol Dept Civil Engn Coimbatore India

Kerala experiences a high rate of annual rainfall and flooding resulting in a frequent natural disaster. The objective of this study is to develop flood susceptibility maps for the Idukki district making use of Remote Sensing (RS) data, Geographic Information System (GIS), and Machine Learning (ML). In this study, five different ML models (Adaboost, Gradient boosting, Extreme Gradient boosting (XGB), CatBoost, Stochastic Gradient boosting (SGB)) are evaluated to deter-mine flood susceptibility in Idukki district Kerala. There were a total of sixteen hydrometeoro-logical parameters taken into account. Area under the curve (AUC) was used to evaluate the accuracy of various techniques in terms of both prediction and success rates. The validation re-sults proved the efficiency of the individual techniques. The highest AUC was obtained by the SGB and GBC (92%), followed by that of the Adaboost with AUC 87%, and the lowest AUC was ob-tained by CatBoost, with AUC of 79%. The absence of data overfitting in all models demonstrates the efficacy of boosting techniques. The boosting algorithms penalize models that overfit the training set, which helps to decrease overfitting. Researchers and local governments could benefit from the proposed boosting techniques in the flood susceptibility mapping and mitigation strategies.

关键词： Machine learning Flood Idukki boosting algorithm GEE SAR

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An efficient version of inverse boosting for classification

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2013年第2期35卷 188-199页

作者： Gao, Jingyang Chen, Chenglizhao Zhen, Dantong Zhu, Qunxiong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

This paper gives a survey of the inverse boosting algorithm which employs an inverse error vector to produce new sample distributions. The experimental result shows that the inverse boosting can outperform normal boosting in some cases. Further experiments on several benchmark real-world data sets available from the UCI repository are taken to get a better insight into the inverse boosting algorithm, and then we find that the performance of the ensemble is determined by the performance of its base classifiers. This distinctive feature of inverse boosting indicates that traditional ways to improve the performance of an ensemble through enhancing the diversity between the base classifier are invalid here. Therefore, this paper proposes a method with pertinence to improve the performance of inverse boosting.

关键词： boosting algorithm diversity inverse error vector pattern recognition

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Salient object detection via boosting object-level distinctiveness and saliency refinement

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2017年 48卷 224-237页

作者： Yan, Xiaoyun Wang, Yuehuan Song, Qiong Dai, Kaiheng Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Natl Key Lab Sci & Technol Multi Spectral Informa Wuhan 430074 Hubei Peoples R China

Many salient object detection approaches share the common drawback that they cannot uniformly highlight heterogeneous regions of salient objects, and thus, parts of the salient objects are not discriminated from background regions in a saliency map. In this paper, we focus on this drawback and accordingly propose a novel algorithm that more uniformly highlights the entire salient object as compared to many approaches. Our method consists of two stages: boosting the object-level distinctiveness and saliency refinement. In the first stage, a coarse object-level saliency map is generated based on boosting the distinctiveness of the object proposals in the test images, using a set of object-level features and the Modest AdaBoost algorithm. In the second stage, several saliency refinement steps are executed to obtain a final saliency map in which the boundaries of salient objects are preserved. Quantitative and qualitative comparisons with state-of-the-art approaches demonstrate the superior performance of our approach. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Saliency Salient object detection Object-level distinctiveness boosting algorithm Saliency refinement

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Intelligent Impulse Finder: A boosting multi-kernel learning network using raw data for mechanical fault identification in big data era

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ISA TRANSACTIONS 2020年 107卷 402-414页

作者： Chen, Jinglong Chang, Yuanhong Qu, Cheng Zhang, Mingquan Li, Fudong Pan, Jun Xi An Jiao Tong Univ State Key Lab Mfg & Syst Engn Xian 710049 Peoples R China

Nowadays, most intelligent diagnosis methods focus on fault classification and the discriminative knowledge is unknown due to the 'black box' characteristic. However, impulse responses in vibration signals, which is important sign to determine whether mechanical equipment is faulty, are rarely studied under intelligent methods since their recognition is both difficult and time-consuming, especially mixed with noise. Aiming at these problems, a novel impulse recognition method was proposed to capture them from raw mechanical data. Firstly, a single-kernel convolutional neural network is proposed as weak classifier to learn discriminative information from raw data. Then, a coarse-to-fine search is proposed to locate position of impulse response. Finally, the boosting algorithm is used to ensemble several proposed weak classifiers for final output. Vibration signals of bearings with two different faults are utilized to validate the proposed model. The results prove that the proposed approach obtain higher accuracy compared with traditional Laplace wavelet method. Moreover, the extracted kernel functions reveal new knowledge about characteristics of impulse responses, which significantly differs from traditional hypothesis and sheds a light on improvement of relevant approaches. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Transient impulse Rolling element bearings Convolutional neural network boosting algorithm

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