Suchergebnisse - "Handling Imbalanced Data in Classification Problems"

A Logistic Regression Model to Predict Malaria Severity in Children

Autoren: Mary Opokua Ansong Asare Yaw Obeng Samuel King Opoku

Quelle: European Journal of Electrical Engineering and Computer Science. 8:31-35

Schlagwörter: Immunology, Handling Imbalanced Data in Classification Problems, Logistic regression, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Automated Analysis of Blood Cell Images, Artificial Intelligence, 11. Sustainability, FOS: Mathematics, Psychology, 0105 earth and related environmental sciences, Malaria Parasite Detection, FOS: Clinical medicine, Statistics, 1. No poverty, 15. Life on land, Malaria, 3. Good health, FOS: Psychology, 13. Climate action, Computer Science, Physical Sciences, Medical Image Analysis, Medicine, Computer Vision and Pattern Recognition, Mathematics

IA evaluadora de manos de poker

Autoren: Estith Bryan Vargas Quispe Eybert Macedo Pillco Juan Carlos Quispe Ttito et al.

Quelle: Innovación y Software, Vol 5, Iss 1 (2024)

Schlagwörter: poker hand, poker texas, Handling Imbalanced Data in Classification Problems, red neuronal, 02 engineering and technology, predicción, Artificial Intelligence, Computer Science, Physical Sciences, 0202 electrical engineering, electronic engineering, information engineering, dataset, T1-995, Technology (General), Art

Zugangs-URL: https://doaj.org/article/df61084e61ca4073992c06001736b177

An Improved Model Using Oversampling Technique and Cost-Sensitive Learning for Imbalanced Data Problem

Autoren: Shrouk El-Amir Ibrahim Elhenawy

Quelle: Information Sciences with Applications. 2:33-50

Schlagwörter: Artificial intelligence, Computer network, Artificial Intelligence, 4. Education, Computer Science, Physical Sciences, Oversampling, Machine learning, Handling Imbalanced Data in Classification Problems, Computer science, Bandwidth (computing)

An Application of Multinomial Mis-Classification Cost Matrix For A P- To – P Lending Credit Score

Autoren: Daniel K. Bakker Oyaro Jackson Ong'eta

Quelle: Advances in Social Sciences Research Journal. 11:76-89

Schlagwörter: Credit card, Profit (economics), Economics, Profitability index, Actuarial science, Bankruptcy Prediction and Credit Scoring Models, Social Sciences, Handling Imbalanced Data in Classification Problems, Business, Management and Accounting, Set (abstract data type), FOS: Economics and business, Multinomial distribution, Artificial Intelligence, Accounting, Machine learning, Microeconomics, Business, Econometrics, Payment, Credit risk, Credit Scoring, Cost-Sensitive Learning, 1. No poverty, Computer science, Programming language, Multinomial logistic regression, Computer Science, Physical Sciences, Credit history, Credit score, Finance

An Enhanced Ant Colony Optimisation Algorithm with the Hellinger Distance for Shariah-Compliant Securities Companies Bankruptcy Prediction

Autoren: Annuur Zakiah Zainol Rizauddin Saian Teoh Yeong Kin et al.

Quelle: Journal of ICT, Vol 23, Iss 1 (2024)

Schlagwörter: Hellinger distance, Artificial intelligence, Shariah-compliant securities, QA75 Electronic computers. Computer science, Bankruptcy Prediction and Credit Scoring Models, Social Sciences, Handling Imbalanced Data in Classification Problems, Bankruptcy Prediction, Business, Management and Accounting, Information technology, Ant colony optimization, Artificial Intelligence, Accounting, FOS: Mathematics, Bankruptcy prediction, Business, bankruptcy prediction, Bankruptcy, Computer network, Imbalanced Data, Statistics, 1. No poverty, T58.5-58.64, Computer science, 3. Good health, Algorithm, Computer Science, Physical Sciences, 8. Economic growth, ANT, Finance, Mathematics

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/d648b0a78d3b4a8ba3d40d6320266d18

Federated learning model for credit card fraud detection with data balancing techniques

Autoren: Mustafa Abdul Salam Khaled M. Fouad Doaa L. El-Bably et al.

Quelle: Neural Computing and Applications. 36:6231-6256

Schlagwörter: Resampling, Artificial intelligence, Credit card, Class (philosophy), Credit card fraud, Fraud Detection, Handling Imbalanced Data in Classification Problems, Bankruptcy Prediction and Credit Scoring Models, Social Sciences, Business, Management and Accounting, 02 engineering and technology, Database, Engineering, Artificial Intelligence, Accounting, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, Decision tree, 0202 electrical engineering, electronic engineering, information engineering, Business, Electrical and Electronic Engineering, Payment, Order (exchange), Database transaction, Cost-Sensitive Learning, Electricity Theft Detection in Smart Grids, Transaction data, 1. No poverty, Computer science, World Wide Web, Detection, Computer Science, Physical Sciences, 8. Economic growth, Finance, Random forest

Fraud Transaction Detection For Anti-Money Laundering Systems Based On Deep Learning

Autoren: Jorge Felix MARTÍNEZ PAZOS Jorge GULÍN GONZÁLEZ David BATARD LORENZO et al.

Quelle: Volume: 3, Issue: 129-34
Journal of Emerging Computer Technologies

Schlagwörter: Artificial intelligence, Sociology and Political Science, Fraud Detection, Money laundering, Generalizability theory, Handling Imbalanced Data in Classification Problems, Social Sciences, 02 engineering and technology, Quantum mechanics, Software and Application Security, Artificial Intelligence, Computer security, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Business, Yazılım ve Uygulama Güvenliği, Financial transaction, Database transaction, Cost-Sensitive Learning, SAFER, Physics, Statistics, 1. No poverty, Deep learning, Power (physics), 16. Peace & justice, Computer science, Programming language, AMLs, Deep Learning, Dense Neural Networks, Financial Fraud, Fraud Transaction Detection, Reliability (semiconductor), Computer Science, Physical Sciences, 8. Economic growth, Organized Crime and Criminal Networks Analysis, Finance, Mathematics

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/ject/issue/77437/1428146

Breaking new ground in cardiovascular heart disease Diagnosis K-RFC: An integrated learning approach with K-means clustering and Random Forest classifier

Autoren: Ahmed Hamza Osman Ashraf Osman Ibrahim Abeer Alsadoon et al.

Quelle: AIMS Mathematics, Vol 9, Iss 4, Pp 8262-8291 (2024)

Schlagwörter: Artificial intelligence, Health Professions, Handling Imbalanced Data in Classification Problems, heart disease, 02 engineering and technology, Pattern recognition (psychology), 03 medical and health sciences, 0302 clinical medicine, Cluster analysis, Health Information Management, Artificial Intelligence, Health Sciences, Machine learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, Disease Risk Prediction, classifier, Machine Learning in Healthcare and Medicine, Deep Learning Applications in Healthcare, k-means clustering, Computer science, 3. Good health, Computer Science, Physical Sciences, Heart Disease Prediction, Classifier (UML), diagnosing, random forest, Mathematics, Random forest

Zugangs-URL: https://doaj.org/article/ac29c139f2364f18b06fdf7f49537e15

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

Autoren: Suraya Nurain Kalid Kok-Chin Khor Keng-Hoong Ng et al.

Quelle: IEEE Access, Vol 12, Pp 23636-23652 (2024)

Schlagwörter: Economics and Econometrics, Artificial intelligence, Credit card, Class (philosophy), Fraud Detection, Handling Imbalanced Data in Classification Problems, Bankruptcy Prediction and Credit Scoring Models, Social Sciences, PRISMA, Business, Management and Accounting, Artificial Intelligence, Computer security, Accounting, credit instruments, Business, Smart card, Payment, Data mining, overlapping classes, credit card fraud, Credit Scoring, Microfinance, Gender Empowerment, and Economic Development, Imbalanced Data, Default, 1. No poverty, imbalanced class distribution, Computer science, TK1-9971, payment default, World Wide Web, HG3691-3769 Credit. Debt. Loans. Including credit institutions, Economics, Econometrics and Finance, bankruptcy, consumer credit, Computer Science, Physical Sciences, 8. Economic growth, Electrical engineering. Electronics. Nuclear engineering, Finance

Dateibeschreibung: text

Zugangs-URL: https://doaj.org/article/9151ec217850430b974e1cc54d7ff5f1

A novel profit-based validity index approach for feature selection in credit risk prediction

Autoren: M.C.S. Pang Zhe Li

Quelle: AIMS Mathematics, Vol 9, Iss 1, Pp 974-997 (2024)

Schlagwörter: Artificial intelligence, Economics, Feature (linguistics), Actuarial science, Bankruptcy Prediction and Credit Scoring Models, Social Sciences, Handling Imbalanced Data in Classification Problems, 02 engineering and technology, 0202 electrical engineering, electronic engineering, information engineering, Microeconomics, Credit Ratings, Credit Scoring, Profit maximization, Credit Spread Changes, Default, Mathematical optimization, Warning system, 1. No poverty, Predictive Modeling, FOS: Philosophy, ethics and religion, Economics, Econometrics and Finance, feature combination, Physical Sciences, Feature selection, 8. Economic growth, Telecommunications, credit risk, Profit (economics), Fraud Detection, Business, Management and Accounting, validity index, feature weights, FOS: Economics and business, loan profit, Artificial Intelligence, Accounting, QA1-939, FOS: Mathematics, Econometrics, Credit risk, Linguistics, Computer science, Loan, Philosophy, Computer Science, Determinants of Credit Risk in Financial Markets, Maximization, FOS: Languages and literature, Finance, Mathematics

Zugangs-URL: https://doaj.org/article/7e159392c2f648a38be2a1ce74a690e9

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

Autoren: Durr e Nayab Rehan Ullah Khan Ali Mustafa Qamar

Quelle: Applied Computational Intelligence and Soft Computing, Vol 2023 (2023)

Schlagwörter: Artificial neural network, Artificial intelligence, Support vector machine, Handling Imbalanced Data in Classification Problems, Health Professions, 02 engineering and technology, Boosting (machine learning), Pattern recognition (psychology), Bayesian probability, Learning with Noisy Labels in Machine Learning, Boosting, Bayes' theorem, Health Information Management, Artificial Intelligence, Support Vector Machines, Meta-Learning, Health Sciences, Machine learning, Decision tree, 0202 electrical engineering, electronic engineering, information engineering, Multilayer perceptron, Perceptron, Machine Learning in Healthcare and Medicine, Naive Bayes classifier, AdaBoost, QA75.5-76.95, 15. Life on land, Computer science, Random subspace method, 3. Good health, Electronic computers. Computer science, Computer Science, Physical Sciences, Classifier (UML), Robust Learning, Random forest

Dateibeschreibung: text/xhtml

Zugangs-URL: https://doaj.org/article/6c6ac4c6cb1f463a848e65d30d56be90

Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification

Autoren: Aishat Salau Nnanna Agwu Nwojo Moussa Mahamat Boukar et al.

Quelle: Engineering, Technology & Applied Science Research. 13:12205-12210

Schlagwörter: Incremental Learning, Artificial intelligence, Handling Imbalanced Data in Classification Problems, 02 engineering and technology, Systems engineering, Task (project management), Engineering, Artificial Intelligence, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Adaptation to Concept Drift in Data Streams, Clinical Event Prediction, Disease Risk Prediction, Data mining, Cost-Sensitive Learning, 4. Education, Deep Learning Applications in Healthcare, Medical Concept Embedding, Deep learning, Active learning (machine learning), Computer science, Process (computing), 3. Good health, Operating system, Computer Science, Physical Sciences

Financial Fraud in China Concept Stock: Identification, Reasons, and Solutions

Autoren: Zhixia Ding Siying Chen Haiying Ma

Quelle: Advances in Economics, Management and Political Sciences. 32:86-97

Schlagwörter: China, 9. Industry and infrastructure, Financial fraud, FOS: Political science, Fraud Detection, Actuarial science, 1. No poverty, Handling Imbalanced Data in Classification Problems, FOS: Mechanical engineering, FOS: Law, Mechanical engineering, 12. Responsible consumption, Engineering, Artificial Intelligence, Accounting, Computer Science, Physical Sciences, 8. Economic growth, Stock (firearms), Business, Work (physics), Political science, Law, Finance

Using machine learning ensemble method for detection of energy theft in smart meters

Autoren: Kawoosa, Asif Iqbal Prashar, Deepak Faheem, Muhammad et al.

Weitere Verfasser: Kawoosa, Asif Iqbal Prashar, Deepak Faheem, Muhammad et al.

Quelle: IET Generation, Transmission & Distribution, Vol 17, Iss 21, Pp 4794-4809 (2023)

Schlagwörter: TK1001-1841, Artificial intelligence, Support vector machine, False positive paradox, Fraud Detection, 0211 other engineering and technologies, Handling Imbalanced Data in Classification Problems, fi=Tietotekniikka|en=Computer Science, TK3001-3521, 02 engineering and technology, electricity supply industry, Boosting (machine learning), 7. Clean energy, Production of electric energy or power. Powerplants. Central stations, Engineering, Electricity, Artificial Intelligence, Ensemble learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Decision tree, Leak Detection, False positive rate, Electrical and Electronic Engineering, Civil and Structural Engineering, ta113, Distribution or transmission of electric power, ta213, Electricity Theft Detection in Smart Grids, Design and Management of Water Distribution Networks, AdaBoost, smart meters, Computer science, Detection, Electrical engineering, Physical Sciences, Computer Science, Gradient boosting, Electricity Theft, Random forest

Dateibeschreibung: fi=kokoteksti; en=fulltext; true

Zugangs-URL: https://doaj.org/article/13305cfcf4fe4f87ab49c9c6abae4ed5

Electricity theft detection for energy optimization using deep learning models

Autoren: Pamir Nadeem Javaid Muhammad Umar Javed et al.

Quelle: Energy Science & Engineering, Vol 11, Iss 10, Pp 3575-3596 (2023)

Schlagwörter: Technology, Artificial intelligence, Microgrid, Science, long short‐term memory, 0211 other engineering and technologies, Handling Imbalanced Data in Classification Problems, electricity theft detection, Control (management), 02 engineering and technology, Smart grid, 7. Clean energy, Engineering, Electricity, Artificial Intelligence, Machine learning, smart grids, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Edge Computing, Electrical and Electronic Engineering, Hyperparameter, Cost-Sensitive Learning, 9. Industry and infrastructure, Electricity Theft Detection in Smart Grids, deep learning, Deep learning, Autoencoder, Computer science, convolutional autoencoder, Wireless Communication Technologies, Detection, 13. Climate action, Electrical engineering, Physical Sciences, Computer Science, weighting, Electricity Theft, Supervised Learning

Zugangs-URL: https://doaj.org/article/8171e0420e324ed0b420c341441dcf1e

Fast and Informative Model Selection Using Learning Curve Cross-Validation

Autoren: Felix Mohr Jan N. van Rijn

Quelle: IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:9669-9680

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Semi-Supervised Learning, Handling Imbalanced Data in Classification Problems, Active Learning, 02 engineering and technology, Model selection, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Context (archaeology), Selection (genetic algorithm), Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Data mining, Biology, Cost-Sensitive Learning, Paleontology, Cross-validation, Computer science, Process (computing), Operating system, Computer Science, Physical Sciences, Algorithms, Learning Curve, Robust Learning, Labeled data, Training set

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37028368
http://arxiv.org/abs/2111.13914

Using the Beneish M-score Model to Detect Financial Statement Fraud in the Microfinance Industry in Ghana

Autoren: Nicholas Adoboe-Mensah Hussein Salia Emmanuel Addo

Quelle: International Journal of Economics and Financial Issues. 13:47-57

Schlagwörter: Economics, 9. Industry and infrastructure, Fraud Detection, Financial statement, 1. No poverty, Handling Imbalanced Data in Classification Problems, Microfinance, Audit, 12. Responsible consumption, Artificial Intelligence, Earnings, Literature, Accounting, Language change, Computer Science, Physical Sciences, 8. Economic growth, Business, Economic growth, Art

HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES

Autoren: Mouna Tarik Ayoub Mniai Khalid Jebari

Quelle: Applied Computer Science, Vol 19, Iss 2, Pp 112-124 (2023)

Schlagwörter: Artificial intelligence, Machine Fault Diagnosis and Prognostics, Support vector machine, Data pre-processing, Feature (linguistics), Handling Imbalanced Data in Classification Problems, FOS: Mechanical engineering, Information technology, 02 engineering and technology, features selection, Reliability engineering, Bandwidth (computing), predictive maintenance, Engineering, Selection (genetic algorithm), Artificial Intelligence, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, support vector machine, Ensemble Methods, Data mining, Preprocessor, Computer network, Mechanical Engineering, Oversampling, Comminution in Mineral Processing, Predictive maintenance, Linguistics, smote-tomek, QA75.5-76.95, T58.5-58.64, Computer science, FOS: Philosophy, ethics and religion, Downtime, Philosophy, Operating system, machine learning, Control and Systems Engineering, Electronic computers. Computer science, Computer Science, Physical Sciences, Feature selection, FOS: Languages and literature, Random forest

Zugangs-URL: https://doaj.org/article/5138918d6b604dd09dc0d9394af1e690

Factors Affecting Cheating Behavior at Quetta- Pakistan

Autoren: Muneeb Ur Rahman

Quelle: Journal of Education and Educational Development. 10:139-159

Schlagwörter: 9. Industry and infrastructure, 4. Education, 1. No poverty, Social Sciences, Handling Imbalanced Data in Classification Problems, Globe, Social psychology, Mathematics education, FOS: Psychology, Situational ethics, Artificial Intelligence, Computer Science, Physical Sciences, Cheating, Psychology, Research Misconduct and Academic Integrity, 10. No inequality, Safety Research, Cheating Behavior, Neuroscience

A high-quality feature selection method based on frequent and correlated items for text classification

Autoren: Heba Mamdouh Farghaly Tarek Abd El‐Hafeez

Quelle: Soft Computing. 27:11259-11274

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Metric (unit), Feature (linguistics), FOS: Political science, Handling Imbalanced Data in Classification Problems, FOS: Law, 02 engineering and technology, Epistemology, Pattern recognition (psychology), Detection and Prevention of Phishing Attacks, Selection (genetic algorithm), Engineering, Artificial Intelligence, Multi-label Text Classification in Machine Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Feature Selection, Multi-label Learning, Text Classification, Data mining, Political science, Cost-Sensitive Learning, Linguistics, Computer science, Process (computing), FOS: Philosophy, ethics and religion, Philosophy, Operating system, Spam Detection, Operations management, Computer Science, Physical Sciences, Feature selection, Quality (philosophy), FOS: Languages and literature, Relevance (law), Law, Information Systems