Showing 1-25 of 5,731 resultsfor "Index Terms":ensemble classifier
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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 (3)
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
Enhancing Heart Disease Prediction and Analysis: An Efficient Voting Ensemble model
Sasank Talapaneni;Chaitanya Sai Kota;Narahari Yalagala;Rakesh Nunna;Radha Mothukuri
2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)
Year: 2024 | Conference Paper |
The prediction of heart disease is of significant importance in the domain of healthcare, where accurate diagnosis can save lives. In this paper, we propose a proficient model for prediction of heart disease using a Voting Ensemble technique. We employ a integration of Logistic Regression, Decision Tree classifier, and Random Forest classifiers to create a robust predictive model. This ensemble me...Show More
Water Quality Assessment Using Ensemble Technique
Suman M. Patil;Jessina James;Anupama P. Bidargaddi;Shreyanka Padennavar;Sunidhi S. Yaligar
2024 IEEE Conference on Engineering Informatics (ICEI)
Year: 2024 | Conference Paper |
Compromised water quality poses risks to human health which necessitates the need for accurate water quality assessment. Our paper employs ensemble machine learning to accurately classify water samples. We present a comprehensive study focusing on the application of machine learning algorithms for the classification of water samples to determine their suitability for human consumption. The soft vo...Show More
Detection of Disaster Situational Awareness Tweets Using Ensemble Learning
2024 IEEE Conference on Computer Applications (ICCA)
Year: 2024 | Conference Paper |
Nowadays, people have embraced social media for more complex purposes such as communicating for update and reliable news during a disaster. Social media has developed into an essential information channel in recent years that can be utilized to improve disaster response. Due to the high feature dimensionality, categorizing situational awareness tweets based on user posts is a challenging procedure...Show More
Classification of Breast Cancer using User-Defined Weighted Ensemble Voting Scheme
TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
When weak classifiers, i.e., estimators, are not justifying the classification of breast cancer then ensemble learning is a way to improve the classification of cancer. The ensemble is basically an aggregator where all weak classifiers are merged to get a strong classifier. The ensemble is based on a majority voting scheme. A hard voting scheme is used to take a major vote of each classifier where...Show More
VC-IDS: An Ensemble Learning Method based on Voting Classifier for Intrusion Detection System using Machine Learning Algorithms
Md Istakiak Adnan Palash;Sana Ulla;A. K.M. Mohaiminul Islam;Sabbir Ahamed Mridha;Md Jaber Al Nahian;M. Shamim Kaiser
2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The number of illegal intruders is growing at the same rate as the number of computer networks and the amount of data on those networks. They try to steal important and private information from the network about its users. An intrusion detection system (IDS) blocks all malicious or unauthorized activity from a computer network. However, it is important to detect illegal activity as early as possib...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
Enhancing Spam Email Detection with a Soft Voting Ensemble of Optimized Machine Learning
Asif Tazwar;Md Mahir Daiyan;Md Jiabul Hoque;Mohammed Saifuddin;Md. Khaliluzzaman
2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)
Year: 2024 | Conference Paper |
Spam email detection is crucial for cybersecurity, as it protects user privacy and reduces security risks. The persistent presence of spammers necessitates continuous improvements in spam filtering measures. To address this challenge, this study employs Grid Search Optimizer to fine-tune the parameters of four distinct classifiers: Support Vector Machine (SVM), Random Forest, Naive Bayes, and XGBo...Show More
A Novel Ensemble Machine Learning Algorithm for Predicting the Suitable Crop to Cultivate Based on Soil and Environment Characteristics
Research in agriculture is a promising field, and crop prediction for particular land areas is especially critical to agriculture. Such prediction depends on the soil, minerals, and environment, the last of which has been short-changed by changing climatic conditions. Consequently, crop prediction for a particular zone presents difficulties for farmers. This is where machine learning (ML) steps in...Show More
An Improved Bayesian Learner Based on Optimized Kernel Density Estimation and Wasserstein Soft Ensemble Strategy
Zhiwei Ye;Yuanhu Liu;Jiazhi Lv;Yingying Liu;Zhenwei Wu;Wanfang Bai
Bayesian classification is a common data analysis and modeling method in data mining. In this paper, an improved ensemble method and the optimized Kernel density estimation used to Bayesian classifier. Unlike the traditional Bayesian classifier in that the conditional possibility density is calculated by an assumed statistical model, our method estimated the possibility value without model assumpt...Show More
Anomaly Based Network Intrusion Detection Using Ensemble Classifiers
Md. Showrov Hossen;Mohammad Jamal Hossain;Md. Abdul Masud;Md. Samsuzzaman;Chinmay Bepery
2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)
Year: 2022 | Conference Paper |
Network Intrusion Detection System is extensively utilized for protection and reducing the damages of information system. It protects threats and vulnerabilities in computer network. Due to the rapid growth of computer network communications, network intrusion is significantly increased and the intrusion detection is considered as a major issue in nowadays. For secure the communication, it is nece...Show More
A Decision Tree Classifier Based Ensemble Approach to Credit Score Classification
2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The process of classifying credit scores holds a crucial role in evaluating an individual's creditworthiness, influencing significant financial choices. This study is driven by the dynamic nature of credit scores and the financial sector's need for precise, real-time credit evaluations. This research introduces an ensemble-based method for credit score classification, utilizing a blend of diverse ...Show More
Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Parkinson’s Disease Detection
2022 3rd International Conference on Computing, Analytics and Networks (ICAN)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
A neurological illness called Parkinson's disease (PD) commonly appears between the ages of 55 and 65. Moreover, a patient's entire quality of life is significantly impacted by the progressive development of motor as well as non-motor symptoms due to this disease. There is no known cure for PD, although a number of therapies have been created to assist control its symptoms. Therefore, the manageme...Show More
Cyberbullying Detection and Hate Speech Identification using Machine Learning Techniques
2022 Second International Conference on Interdisciplinary Cyber Physical Systems (ICPS)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Bullying has been prevalent since the beginning of time, It’s just the ways of bullying that have changed over the years, from physical bullying to cyberbullying. According to Williard (2004), there are eight types of cyberbullying such as harassment, denigration, impersonation, etc. It’s been around 2 decades since social media sites came into the picture, but there haven’t been a lot of effectiv...Show More
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
Gonzalo Martínez-Muñoz;Daniel Hernández-Lobato;Alberto Suárez
Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, th...Show More
Predicting The Recurrence of Colorectal Cancer in Patients Using Ensemble Methods
Y. Jeevan Nagendra Kumar;Ridhi Vyas;B. Hruthika;B. Seershika;T. Pranitha
2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT)
Year: 2023 | Conference Paper |
Among the top ten death-causing diseases in the world, cancer isconsidered to be one. There are about 100 different forms of cancer, and cancer prediction is critical to the advancement of data mining applications. The development of adequate methods for locating cancer instances will go a long way toward improving cancer patient therapy diagnosis. The term “cancer” refers to a group of disorders....Show More
Spammer Classification Using Ensemble Methods over Structural Social Network Features
2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
Year: 2014 | Conference Paper |
Cited by: Papers (16)
The overwhelming growth and popularity of online social networks is also facing the issues of spamming, which mainly leads to uncontrolled dissemination of malware/viruses, promotional ads, phishing, and scams. It also consumes large amounts of network bandwidth leading to less revenue and significant financial losses to organizations. In literature, various machine learning techniques have been e...Show More
Hybrid CNN-Ensemble based Classifier for Touchless Fingerprint Classification
2021 IEEE Mysore Sub Section International Conference (MysuruCon)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The outstand ascent in software computing has expanded the skyline for various applications to make our life easy in decision making. Among the significant requests, security frameworks have consistently been the predominant one to guarantee authenticity. Fingerprint classification techniques have acquired wide-spread consideration for personalized authentication. Automated fingerprint classificat...Show More
Automated Fever Detection by Ensemble Learning Technique: An Investigative Study
2023 Seventh International Conference on Image Information Processing (ICIIP)
Year: 2023 | Conference Paper |
Fever is a common symptom for many infectious and inflammatory conditions. Manual microscopy diagnosis of fever is time-consuming and error-prone. This paper investigates automated ensemble learning approaches for classifying fever using cell images. Methods like discrete cosine transform (DCT), principal component analysis (PCA), gray level co-occurrence matrix (GLCM), and local binary patterns (...Show More
Enhancing Anomaly Detection in Electric Vehicle Supply Equipment (EVSE) Networks Using Classical and Ensemble Learning Approaches
Sandeep R. Hegde;Vijayabhaskaramoorthy V.;K. R. M. Vijaya Chandrakala;Gireesh Kumar T.;V. K. Arun Shankar
2024 Control Instrumentation System Conference (CISCON)
Year: 2024 | Conference Paper |
Electric Vehicle Supply Equipment (EVSE) networks, integral to modern transportation systems, are vulnerable to cyber threats due to integration into smart grids and IoT ecosystems. EVSE network A represents a traditional, centralized EV charging infrastructure, whereas EVSE network B denotes a distributed, decentralized charging network with varying levels of connectivity. This research investiga...Show More
Enhancing DNA Sequence Alignment using Ensemble Learning: A Voting Classifier and Stacking Approach
2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
The aim of the paper is to improve pairwise DNA sequence alignment accuracy using ensemble learning techniques. The application of these methods has resulted in a remarkable accuracy of 94.5%. Utilizing the DNA sequence alignment datasets based on NW algorithm," we combine the outputs of multiple alignment algorithms through Voting Classifier and Stacking Ensemble methods to enhance alignment prec...Show More
“Ensemble Incremental Model” for Misinformation Detection in Healthcare
2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA)
Year: 2023 | Conference Paper |
With the growth of social media usage, information sharing has become a concern due to the spread of misinformation and its strong impact on society. Researchers have developed innovative techniques to detect and classify false information online. Improving the accuracy of these models using ensemble learning methods has gained popularity recently. However, these ensemble models are based on stand...Show More
Effective Analytics on Healthcare Big Data Using Ensemble Learning
2020 IEEE Conference on Computer Applications(ICCA)
Year: 2020 | Conference Paper |
Cited by: Papers (19)
Healthcare big data is a collection of record of patient, hospital, doctors and medical treatment and it is so large, complex, distributed and growing so fast that this data is difficult to maintain and analyze using some traditional data analytics tools. To solve this difficulties, some machine learning tools are applied on such big amount of data using big data analytics framework. In recent yea...Show More
An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach
Sapna Singh Kshatri;Deepak Singh;Bhavana Narain;Surbhi Bhatia;Mohammad Tabrez Quasim;G. R. Sinha
Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an app...Show More
Ensemble Machine Learning for Multi-Class Classification Tasks
Nahian Sourov;Faiad Hossain Chowdhury;A.S.M Redowan;Md Tarifuzzaman;Dewan Md. Farid
2023 26th International Conference on Computer and Information Technology (ICCIT)
Year: 2023 | Conference Paper |
Ensemble learning is the process of amalgamating multiple classifiers to engender a strong classifier in supervised machine learning. It boost-up the performance week learning algorithms. In this paper, we have tested the performance of RandonForest, Bagging, and Boosting methods applying decision tree, Naïve Bayes classifier, and NBTree as a base classifier on 10 benchmark datasets. RandomForest ...Show More
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