A5049904007- Data Stream Mining Techniques
- Machine Learning and Data Classification
- Cardiovascular Function and Risk Factors
- Nutrition and Health in Aging
- Internet Traffic Analysis and Secure E-voting
- Network Security and Intrusion Detection
- Advanced Measurement and Detection Methods
- Sensor Technology and Measurement Systems
- Diabetes Treatment and Management
- Body Composition Measurement Techniques
- Sodium Intake and Health
- Extracellular vesicles in disease
- Cardiovascular Disease and Adiposity
- Blood Pressure and Hypertension Studies
- Inertial Sensor and Navigation
- Autophagy in Disease and Therapy
- Advanced Bandit Algorithms Research
- Viral Infections and Immunology Research
- Nutritional Studies and Diet
- Atrial Fibrillation Management and Outcomes
- IL-33, ST2, and ILC Pathways
- Smart Grid Energy Management
- Imbalanced Data Classification Techniques
- Vagus Nerve Stimulation Research
- Fire Detection and Safety Systems
Second Affiliated Hospital of Harbin Medical University
2023-2025
Harbin Medical University
2023-2025
Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University
2020-2024
Wenzhou Medical University
2020-2024
Shandong First Medical University
2024
Second Hospital of Shandong University
2024
Qingdao Municipal Hospital
2022-2024
First Affiliated Hospital of Liaoning Medical University
2024
Qingdao University
2022-2024
National University of Defense Technology
2019-2023
In practical applications, data stream classification faces significant challenges, such as high cost of labeling instances and potential concept drifting. We present a new online active learning ensemble framework for drifting streams based on hybrid strategy that includes the following: 1) an classifier, which consists long-term stable classifier multiple dynamic classifiers (a multilevel sliding window model is used to create update effectively process both gradual drift type sudden...
A challenge to many real-world applications is multiclass imbalance with concept drift. In this paper, we propose a comprehensive active learning method for imbalanced streaming data drift (CALMID). First, design online framework that includes an ensemble classifier, detector, label sliding window, sample windows and initialization training sequence. Next, variable threshold uncertainty strategy based on asymmetric margin matrix designed comprehensively address the problem given class can...
The complex problems of multiclass imbalance, virtual or real concept drift, evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network classification tasks. In this paper, we propose a imbalanced drift framework based on online active learning (MicFoal), which includes configurable supervised learner for the initialization model, an method with hybrid request strategy, sliding window group, sample training weight formula adaptive adjustment...
Glucose metabolic disorder is associated with the risk of heart failure (HF). Adiposity a comorbidity that inextricably linked abnormal glucose metabolism in older individuals. However, effect adiposity on association between and HF risk, underlying mechanism remain unclear. A total 13,251 participants aged ≥ 60 years from cohort study were categorized into euglycemia, prediabetes, uncontrolled diabetes, well-controlled diabetes. was assessed using body mass index (BMI), waist-to-hip ratio...
Practical applications often require learning algorithms capable of addressing data streams with concept drift and class imbalance. This paper proposes an online active paired ensemble for drifting The consists a long-term stable classifier dynamic to address both sudden gradual drift. To select the most representative instances learning, hybrid labeling strategy which includes uncertainty imbalance is proposed. applies margin-based criterion adjustment threshold. Based on categorical...
Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI). The ensemble framework consists of long-term static classifier to handle gradual multiple dynamic classifiers sudden drift. weights the are adjusted from two aspects. First, time-decayed strategy decreases make focus more on new data stream. Second, novel reinforcement mechanism proposed increase...
Applications challenged by the joint problem of concept drift and class imbalance are attracting increasing research interest. This paper proposes a novel Reinforcement Online Active Learning Ensemble for Drifting Imbalanced data stream (ROALE-DI). The ensemble classifier has long-term stable dynamic group which applies reinforcement mechanism to increase weight classifiers, perform better on minority class, decreases opposite. When is imbalanced, classifiers will lack training samples...
To investigate the modifying role of obesity in association between abnormal glucose metabolism and atrial fibrillation (AF) risk older individuals. From April 2007 to November 2011, 11663 participants aged ≥60 years were enrolled Shandong area. Glucose metabolic status determined using fasting plasma hemoglobin A1c levels, body mass index (BMI), waist-to-hip ratio (WHR), visceral fat area (VFA). Obesity-associated activities assessed adiponectin-to-leptin (ALR), galectin-3,...
Parkin (an E3 ubiquitin protein ligase) is an important regulator of mitophagy. However, the role in viral myocarditis (VMC) remains unclear.
The impacts and mechanisms of morning hypertension (MHT) on the risk new-onset atrial fibrillation (AF) in elderly have not been clarified. We aimed to investigate an association between MHT AF explore a mediating effect subclinical inflammation this association.From 2008 2010, 1789 older adults aged ≥60 years were recruited Shandong area, China. Morning blood pressure (BP) was assessed using 24-hour ambulatory BP monitoring. defined as ≥ 135/85 mm Hg during period from wake time 0900 a.m....
Magnetic encoders are widely used in industrial motion control, due to their low-cost, simple structures, and low environmental requirements. However, the obtained quadrature sinusoidal signals suffer from various disturbances, which affects accuracy of magnetic encoders. The current methods combine neural network with phase-locked loop (PLL) typically require knowledge harmonic orders advance use a proportional-integral controller as filter PLL. In this article, we propose new method,...
Exosomes derived from human bone marrow mesenchymal stem cells (BMSCs) play potential protective roles in asthma. However, the underlying mechanisms remain not fully elucidated. Herein, exosomes were isolated BMSCs, and morphology, particle size, exosome marker proteins identified by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), Western blot, respectively. Then airway smooth muscle (ASMCs) treated with transforming growth factor-
This paper proposes an air quality grade forecasting method based on ensemble learning. First, the training data sets are formed of and related meteorological crawled from website. After that, use learning algorithm Leveraging Bagging to learn dataset generate initial model. And model is used make prediction dataset. In total, experiments test both city scale station scale. Experimental results show that proposed has good effect ability real forecast
The complex problems of multiclass imbalance, virtual or real concept drift, evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network classification tasks. In this paper, we propose a m ulticlass i mbalanced c oncept drift f ramework based on o nline ctive l earning (MicFoal), which includes configurable supervised learner for the initialization model, an active learning method with hybrid request strategy, sliding window group, sample training...
Existing clustering algorithms are weak in extracting peak intervals for time series data. In this paper, we propose a new algorithm named Peak Interval Extraction Strategy based Hierarchical Clustering method (PIES_HC) The proposed PIES_HC can effectively exploit inherent interval information of data set to enhance performance. main contributions our work include the design strategy extract and development similarity measure on intervals. With synthetic three real-life sets, experimental...