Showing 1-25 of 15,740 resultsfor "Index Terms":Ensemble Method
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Ensemble Learning Approaches for Automatic Detection of Chronic Kidney Disease Stages during Sleep
Tae Gwan Jang;So Yeon Park;Ji Ae Lee;Ha Young Park;Tserendulam Dorjmaa;Dongwon Kang;Jae Won Yang;Erdenebayar Urtnasan
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
This study investigates the use of ensemble learning methods for the automatic detection of chronic kidney disease (CKD) stages during sleep. We applied and evaluated four ensemble learning approaches—CatBoost, random forest, XGBoost, and LightGBM—to analyze polysomnography (PSG) data from 730 participants of the Cleveland Family Study (539 control subjects and 191 CKD patients). Our analysis invo...Show More
Ensemble Learning Models for Drug Target Interaction Prediction
2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Drug discovery is a crucial phase before drug development since it is the most essential and distinctive means of testing all medications prior to their medical usage. Other processes in drug discovery include drug target interaction predictions, drug repurposing or repositioning, and drug design. Prediction of drug-target interactions is crucial in these cases. Proteins, enzymes, ion channels, an...Show More
An Ordered Aggregation-Based Ensemble Selection Method of Lightweight Deep Neural Networks With Random Initialization
Due to the popularity of 5G connectivity and The Internet of Things sensors, deep learning algorithms are being extended to edge devices. Compared with AI(Artificial Intelligence) cloud platforms, the deployment of deep neural networks on edge devices must focus on low power consumption, low latency, stability and reliability. In recent years, the development of lightweight deep neural network arc...Show More
Comprehensive Framework for Malware Detection: Leveraging Ensemble Methods, Feature Selection and Hyperparameter Optimization
2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Malware detection is a critical task in today's digital landscape, where the proliferation of malicious attacks poses significant threats to data security and privacy. In this paper, we propose a comprehensive framework for malware detection that encompasses various stages, including ensemble model, cross-validation, feature selection methods, and hyperparameter tuning. Through extensive experimen...Show More
Predicting Chronic Obstructive Pulmonary Disease Based on Multi-Stage Composite Ensemble Learning Framework
Zhanxin Gang;Chaoran Jia;Chenhua Guo;Peng Li;Jing Gao;Liang Zhao
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Chronic Obstructive Pulmonary Disease (COPD) severely affects people’s health. With this in mind, we propose a novel Multi-Stage Composite Ensemble Learning Framework (MSCELF) that can diagnose COPD without utilising pulmonary function tests data. Our method explores 12 features from the patients’ baseline data, medical history, blood tests, and arterial blood gas analysis. In the first stage of o...Show More
Multi-model Ensemble Based Approach for Heart Disease Diagnosis
Areesa Ashfaq;Azhar Imran;Inam Ullah;Abdulkareem Alzahrani;Khattab M. Ali Alheeti;Amanullah Yasin
2022 International Conference on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Heart is the core part responsible for blood pumping in the human body. Any disorder that effectuates the functions of the cardiovascular named heart disease or cardiovascular disease. CVD (cardiovascular disease) turns into a major cause of death worldwide. This cardio disease leads to 17.9 million deaths per year according to an estimation by World Health organization. Machine learning approache...Show More
Hyperspectral Image Classification using Ensemble Average Method
2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
A Hyperspectral image, has hundreds of spectral bands as opposed to a classical digital image representation of three spectral bands and therefore provides a more complete part of the light spectrum to display and analyze. Hyperspectral imaging provides the possibility of collecting more precise and comprehensive information than other remotely sensed data. Owing to the immense amount of knowledge...Show More
Cardiac Patient Data Classification Using Ensemble Machine Learning Technique
2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
Heart disease is a major global health problem, and successful treatment and prevention depend greatly on early and correct diagnosis. Machine learning methods have showed promise in the medical data analysis and in helping to categorise diseases. In this article, we investigate the application of ensemble machine learning methods for the categorization of cardiac patient data. Three machine learn...Show More
Approximate Clustering Ensemble Method for Big Data
Mohammad Sultan Mahmud;Joshua Zhexue Huang;Rukhsana Ruby;Alladoumbaye Ngueilbaye;Kaishun Wu
IEEE Transactions on Big Data
Cited by: Papers (11)
Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in distributed computing. A popular method to tackle this problem is to use a random sample of the big dataset to compute an approximate result as an estimation of the true result computed from the entire dataset. In this paper, instead of using a single random sample, we use multiple random samples to compute ...Show More
Outlier Detection using Ensemble Learning
2022 6th International Conference on Information Technology (InCIT)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
The growth of internet in recent years have led to emergence of several types of network attacks and outlier. Hence development of techniques to deal with network attacks and outlier has become an important area of research. Several methods for detecting outliers have been found in the current literature. In this paper, we propose a method for outlier detection which is based on ensemble learning ...Show More
A study of Pareto-based methods for ensemble pool generation and aggregation
2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
In the field of machine learning, the application of ensemble methods is one of the most successful techniques in order to achieve a good performance in classification tasks. The combination of multiple classifiers is able to achieve better results than a single model, and much effort has been put into applying multi-objective optimisation for improving results with diverse ensemble generation and...Show More
A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus Image-Based Cataract Detection
Tabina Shafa Nabila Syahda;Mochamad Nizar Palefi Ma'ady;Berlian Rahmy Lidiawaty
2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS)
Year: 2025 | Conference Paper |
Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. Image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several p...Show More
Ensemble Machine Learning based Weather Prediction System
2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
The technique of predicting the weather using science and technology for a particular area is known as weather forecasting, we employed the ensemble approach to produce more precise results. Ensemble learning enhances machine learning outcomes by mixing numerous models. The predictive performance of this strategy is superior to that of a single model. Many machine learning methods and algorithms c...Show More
An Ensemble Feature Selection Method for Prediction of CKD
2020 International Conference on Computer Communication and Informatics (ICCCI)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
Feature Selection is an important precursor to prediction and classification of medical data. Medical data mining is evolving at a faster rate and the current machine learning algorithms need to be imbibed with intelligent prediction and classification systems to handle the huge medical data. Since the huge volume of data stored in the medical database may be prone to `curse of dimensionality', it...Show More
A Malicious PDF File Detection Method Based on Improved Ensemble Learning Stacking
Yidan Tang;Jinjin Dong;Yixuan Guo;Yihan Zhou;Feifan Lu;Bo Zhang
2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
A malicious PDF file detection method based on ensemble learning is proposed to address the problem that malicious PDF files are highly concealed and difficult to detect. In order to efficiently identify malicious PDF files that are highly concealed, the detection range of malicious PDF files by machine learning models is improved by combining the conventional features of PDF files with structural...Show More
Research on Sampling Diversity Method in Ensemble Learning Base on Margin
2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
From the deviation-variance decomposition and error-bifurcation decomposition, it is found that the diversity of base classifiers in ensemble learning is an important factor to improve the generalization ability and classification accuracy of ensemble learning. It can effectively improve the diversity of ensemble learning by the method of the minimum margin maximization in data samples. An optimal...Show More
Multi-Label Classification of Al-Qur'an Verses Using Ensemble Method and BERT
2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS)
Year: 2025 | Conference Paper |
Multi-label classification is a critical task in text analysis, particularly for complex datasets like Qur‘an verses, which often encapsulate multiple thematic labels. This study investigates the use of ensemble methods by combining traditional machine learning models, such as Support Vector Machine (SVM) and Naïve Bayes, with the transformer-based BERT model. The research evaluates individual and...Show More
Reduced KPCA based Ensemble Learning Approach for Fault Diagnosis of Grid-Connected PV Systems
Khaled Dhibi;Majdi Mansouri;Kais Bouzrara;Hazem Nounou;Mohamed Nounou
2022 19th International Multi-Conference on Systems, Signals & Devices (SSD)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and...Show More
Tree-Based Ensemble Models and Algorithms for Classification
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
An ensemble method is viewed as a compound model. The purpose of such a model is to achieve better predictive performance. The attempt is to tune predictions to observations by decreasing model variance, and bias. First the work focuses at the presentation of the Projective Decision Tree Algorithm (PA), a sort of Decision Tree (DT) based on purity and using the criterion of next node (CNN). Second...Show More
Enhancing Image Super-Resolution with Convolutional Neural Network Ensembles
2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The goal of image super-resolution (SR) is to increase the resolution and quality of low-resolution images, and it is a crucial problem in computer vision. Recent years have seen incredible progress in SR problems using Convolutional Neural Networks (CNNs). However, boosting SR performance even further remains difficult. Using the strength of Convolutional Neural Network ensembles, this research i...Show More
Progressive Heterogeneous Ensemble Learning for Cancer Gene Expression Classification
2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS)
Year: 2022 | Conference Paper |
Ensemble learning is an extensively researched subject in machine learning due to its robust and reliable performance. Multiple machine learning models are combined in ensemble learning to improve performance and reliability. There are many algorithms and variations in ensemble learning, but most techniques focus on data space like Bagging, AdaBoost, etc., or feature space like Random Subspace, At...Show More
Improving Intrusion Detection System Using Ensemble Methods and Over-Sampling Technique
2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
Due to the imbalanced training samples, anomaly-based intrusion detection system (IDS) has to face many problems such as a low detection accuracy, a high false alarm rate and insufficient application value, especially in multi-classification tasks. A variety of methods are proposed in this paper to improve the effect of IDS based on machine learning. Synthetic minority over-sampling technique (SMO...Show More
A Comparative Study of Signal Recognition Based on Ensemble Learning and Deep Learning
2023 International Seminar on Computer Science and Engineering Technology (SCSET)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
In this paper, based on the traditional artificial feature extraction method, the ensemble learning method and the convolutional neural network method are used to identify the types of 9 kinds of analog and digital modulation signals, and the performance is evaluated and compared. It is found that the convolutional neural network has higher SNR tolerance than the ensemble learning algorithm, and h...Show More
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning
Julia Gastinger;Sébastien Nicolas;Dušica Stepić;Mischa Schmidt;Anett Schülke
2021 International Joint Conference on Neural Networks (IJCNN)
Year: 2021 | Conference Paper |
Cited by: Papers (16)
The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models. We demonstrate insights on the power of ensemble learning for forecasting, showing experiment results on about 16000 openly available datasets, from M4, M5, M3 competitions, as well as FRED (Federal Reserve Economic Data) datasets. Whereas...Show More
Ensemble Framework for Big Data Stream Mining
2020 IEEE Conference on Computer Applications(ICCA)
Year: 2020 | Conference Paper |
The rapid development of industry enterprises, the large amount of data generated by these originalities and the exponential growth of industrial business website are the causes that lead to different types of big data and data stream problem. There are many stream data mining algorithms for classification and clustering with their specific properties and significance key features. Ensemble classi...Show More
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