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An ensemble-based transfer learning model for predicting the imbalance heart sound signal using spectrogram images

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 39923页

作者： Singh, Sinam Ajitkumar Devi, Ningthoujam Dinita Singh, Khuraijam Nelson Thongam, Khelchandra Reddy, Balakrishna Majumder, Swanirbhar NIT Manipur Dept Comp Sci & Engn Imphal 795004 Manipur India RIMS Imphal Dept Radiat Oncol Imphal 795004 Manipur India IIIT Nagpur Dept Elect & Commun Engn Nagpur 441108 Maharastra India RGMCET Dept Elect & Commun Engn Nandyal 518501 Andhra Pradesh India Tripura Univ Dept Informat Technol Suryamani Nagar Agartala 799022 Tripura India

Heart sound signal analysis is an important area in healthcare, and the detection of imbalanced heart sounds can provide valuable diagnostic information. However, due to heart sound variability, accurate prediction of imbalanced signals remains challenging. The issue of class inequality has received much attention from numerous scientific domains. The correct classification becomes increasingly challenging as data scale and data imbalance increase. Traditional classifiers tend to favor the dominant class and overlook the minority class, which is frequently considerably more significant when dealing with imbalanced learning problems. We propose an ensemble learning algorithm based on a transfer learning convolutional neural network (CNN) model to solve these challenges to predict imbalanced heart sound signals. We employ spectrogram images and STFT to extract the relevant features from Phonocardiogram (PCG) data. Our model leverages the pre-trained CNN architecture and fine-tunes it on the spectrogram images to improve the prediction performance. Moreover, we incorporate an ensemble approach to improve the model's robustness and accuracy. Our experimental results on a publicly available PhysioNet PCG dataset demonstrate that the proposed algorithm outperforms existing state-of-the-art methods in terms of accuracy, sensitivity, and specificity. The ensemble methodology comprising AlexNet, SqueezeNet, and VGG19 models was proposed and achieved the highest level of performance, resulting in an accuracy of 99.20% and a sensitivity rate of 99.47%. Our study showcases the potential of leveraging technological advancements to predict unbalanced Phonocardiogram (PCG) signals using spectrogram images. This research opens up promising avenues for future exploration in cardiac diagnostics. Specifically, the ensemble-based transfer learning model proposed in this study holds great promise.

关键词： Heard sound recording Spectrogram CNN PhysioNet-2016 Transfer learning ensemble learning algorithm

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An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining

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INFORMATION SCIENCES 2012年 191卷 31-46页

作者： Yu, Lean Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci MADIS Beijing 100190 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China Hangzhou Normal Univ Hangzhou Key Lab E Business & Informat Secur Hangzhou 310036 Zhejiang Peoples R China

In this paper, a novel evolutionary programming (EP) based asymmetric weighted least squares support vector machine (LSSVM) ensemble learning methodology is proposed for software repository mining. In this methodology, an asymmetric weighted LSSVM model is first proposed. Then the process of building the EP-based asymmetric weighted LSSVM ensemble learning methodology is described in detail. Two publicly available software defect datasets are finally used for illustration and verification of the effectiveness of the proposed EP-based asymmetric weighted LSSVM ensemble learning methodology. Experimental results reveal that the proposed EP-based asymmetric weighted LSSVM ensemble learning methodology can produce promising classification accuracy in software repository mining, relative to other classification methods listed in this study. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Asymmetric weighted least squares support vector machine Evolutionary programming ensemble learning algorithm Software repository mining

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The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies

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Nuclear Science and Techniques 2024年第2期35卷 170-182页

作者： Gao-Yi Cheng Qian-Min Su Xi-Guang Cao Guo-Qiang Zhang School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China

Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate *** urgently need solutions to the dilemma of traditional particle identification *** study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate *** intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 ***-based machine learning algorithms and deep learning algorithms *** show excellent performance and generalization *** additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is *** learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies.

关键词： Heavy-ion collisions at low and intermediate energies Machine learning ensemble learning algorithm Particle identification Data imbalance

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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SAFETY RISK EVALUATIONS OF DEEP FOUNDATION CONSTRUCTION SCHEMES BASED ON IMBALANCED DATA SETS

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2020年第4期26卷 380-395页

作者： Gong, Peisong Guo, Haixiang Huang, Yuanyue Guo, Shengyu China Univ Geosci WUHAN Sch Econ & Management Wuhan Peoples R China China Univ Geosci WUHAN Mineral Resource Strategy & Policy Res Ctr Wuhan Peoples R China

Safety risk evaluations of deep foundation construction schemes are important to ensure safety. However, the amount of knowledge on these evaluations is large, and the historical data of deep foundation engineering is imbalanced. Some adverse factors influence the quality and efficiency of evaluations using traditional manual evaluation tools. Machine learning guarantees the quality of imbalanced data classifications. In this study, three strategies are proposed to improve the classification accuracy of imbalanced data sets. First, data set information redundancy is reduced using a binary particle swarm optimization algorithm. Then, a classification algorithm is modified using an Adaboost-enhanced support vector machine classifier. Finally, a new classification evaluation standard, namely, the area under the ROC curve, is adopted to ensure the classifier to be impartial to the minority. A transverse comparison experiment using multiple classification algorithms shows that the proposed integrated classification algorithm can overcome difficulties associated with correctly classifying minority samples in imbalanced data sets. The algorithm can also improve construction safety management evaluations, relieve the pressure from the lack of experienced experts accompanying rapid infrastructure construction, and facilitate knowledge reuse in the field of architecture, engineering, and construction.

关键词： safety risk evaluation construction scheme deep foundation imbalanced data set ensemble learning algorithm machine learning

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An EL-SHAKF-Based Integration Scheme for Gyro Thermal-Magnetic Coupling Heading- Effect Drift Compensation in INS

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IEEE SENSORS JOURNAL 2020年第19期20卷 11508-11517页

作者： Cai, Qingzhong Yang, Changhao Li, Jing Yang, Gongliu Song, Ningfang Yin, Hongliang Liu, Yiliang Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beijing Inst Petrochem Technol Informat Engn Coll Beijing 102617 Peoples R China China Ship Res & Dev Acad Beijing 100192 Peoples R China Natl Remote Sensing Ctr China Beijing 100036 Peoples R China

Inertial navigation system (INS) is widely equipped in various vehicles for overcoming GPS-challenging environments. The thermal-magnetic coupling heading-effect drift (thermal-magnetic drift) of INS is a multi-physics coupling error related to carrier maneuver, which cannot be compensated well by traditional methods. In this paper, the source of gyro thermal-magnetic drift are analyzed and an ensemble learning (EL) and Sage-Husa adaptive Kalman filter (SHAKF) based integration scheme for the thermal-magnetic drift compensation in high-grade INS is established. In the integrated system, the geomagnetic field strength calculated by International Geomagnetic Reference Field (IGRF) model and temperature information measured by sensors are selected as input of the thermal-magnetic drift prediction model. The gyro biases estimated by exponential fading SHAKF are taken as target in training phase. The EL algorithms, LSBoost and Bagging, are used for training when GPS data is available. The gyro thermal-magnetic drift is predicted and compensated by the trained model during GPS outages. Land and sea tests show its effectiveness on compensate the gyro thermal-magnetic drift of the proposed method.

关键词： Optical fiber theory Optical fiber polarization Magnetic fields Predictive models Couplings Temperature measurement Inertial navigation integrated navigation system artificial intelligence ensemble learning algorithm

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Multi-model ensemble for short-term traffic flow prediction under normal and abnormal conditions

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第2期13卷 260-268页

作者： Chen, Xiqun (Michael) Zhang, Shuaichao Li, Li Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Zhejiang Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Accurate traffic flow prediction under abnormal conditions, such as accidents, adverse weather, work zones, and holidays, is significant for proactive traffic control. Here, the authors focus on a special challenge of how to develop robust responsive algorithms and prediction models for short-term traffic forecasting in different traffic conditions. To this end, this study presents an ensemble learning algorithm for the short-term traffic flow prediction via the integration of gradient boosting regression trees (GBRT) and the least absolute shrinkage and selection operator (Lasso). Four different model structures whether considering the feature selection are proposed and tested for multi-step-ahead prediction under both normal and abnormal conditions. The results indicate that the proposed multi-model ensemble models are superior to the benchmark algorithms, i.e., support vector regression, and random forests, the GBRT model outperforms the Lasso model under normal traffic conditions, and the Lasso model has a better prediction accuracy under abnormal traffic conditions. In addition, the Lasso model with the feature selection is superior to the full feature model under either normal or abnormal conditions, while the GBRT model is not always better under normal conditions. The proposed integration framework is general and flexible to assemble various traffic prediction algorithms.

关键词： road traffic traffic engineering computing learning (artificial intelligence) feature selection data analysis regression analysis trees (mathematics) support vector machines multimodel ensemble models short-term traffic flow prediction normal conditions abnormal conditions robust responsive algorithms short-term traffic forecasting traffic conditions ensemble learning algorithm gradient boosting regression trees integration model GBRT model least absolute shrinkage-and-selection operator model Lasso model feature selection multistep-ahead prediction traffic flow data remote traffic microwave sensors urban expressway training set test set support vector regression random forests

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Application of an ensemble learning based Classifier in Crime Prediction

Application of an Ensemble Learning based Classifier in Crim...

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作者： Rui Lu Linying Li Information Department Liaoning Police College Software College Dalian University of Foreign Languages

As an outstanding issue for police,crime prediction has been paid widely attention by *** on ensemble learning method,this paper applies random forest to classifier and introduces a crime prediction method to deeply explore the characteristics of criminal suspects to achieve the purpose of crime *** to calculating attribute importance,the method keeps the important attributes in the *** reduced attribute set is used to train the random forest model to obtain the crime prediction *** crime data was applied to the proposed classifier which is evaluated by the precision and *** experimental results show that the presented classifier is effective.

关键词： crime prediction classifier ensemble learning algorithm random forest data mining

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UniLoc: A Unified Mobile Localization Framework Exploiting Scheme Diversity

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第7期20卷 2505-2517页

作者： Du, Wan Tong, Panrong Li, Mo Univ Calif Merced Dept Comp Sci & Engn Merced CA 95343 USA Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Current localization schemes on mobile devices are experiencing great diversity that is mainly shown in two aspects: the large number of available localization schemes and their diverse performance. This paper presents UniLoc, a unified framework that gains improved performance from multiple localization schemes by exploiting their diversity. UniLoc predicts the localization error of each scheme online based on an error model and real-time context. It further combines the results of all available schemes based on the error prediction results and an ensemble learning algorithm. The combined result is more accurate than any individual schemes. With the flexible design of error modeling and ensemble learning, UniLoc can easily integrate a new localization scheme. The energy consumption of UniLoc is low, since its computation, including both error prediction and ensemble learning, only involves simple linear calculation. Our experience with extensive experiments tells that such easy aggregation incurs little overhead in integrating and training a localization scheme, but gains substantially from the scheme diversity. UniLoc outperforms individual localization schemes by 1.6x in a variety of environments, including > 89% new places where we did not train the error models.

关键词： learning Artificial Intelligence Mobile Radio Radio Direction Finding Telecommunication Computing Uni Loc Unified Mobile Localization Framework Mobile Devices ensemble learning algorithm Localization Error Schemes Error Prediction Modeling Sensors Wireless Fidelity Global Positioning System Smart Phones Mobile Computing Real Time Systems Prediction algorithms

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Correcting bias of satellite rainfall data using physical empirical model

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ATMOSPHERIC RESEARCH 2021年 251卷 105430-105430页

作者： Ziarh, Ghaith Falah Shahid, Shamsuddin Bin Ismail, Tarmizi Asaduzzaman, Md Dewan, Ashraf Univ Teknol Malaysia Sch Civil Engn Dept Water & Environm Engn Fac Engn Johor Baharu Malaysia Staffordshire Univ Dept Engn Sch Digital Sch Digital Technol & Arts Stoke On Trent ST4 2DE Staffs England Curtin Univ Sch Earth & Planetary Sci Spatial Sci Discipline Perth WA 6102 Australia

The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their direct-use can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000-2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict rainfall amount of different intensity class. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete timeseries indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and -3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2-0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro meteorological hazards.

关键词： Near-real-time rainfall Satellite precipitation Bias correction ensemble learning algorithm Physical-empirical model

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