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Handling Class Imbalance and Class Overlap in Machine Learning Applications for Undeclared Work Prediction
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4615-1733-7_30openalex-snapshot
Classification Using Association Rules: Weaknesses and Enhancements
inferredcites →https://doi.org/10.1007/978-3-540-24694-7_32openalex-snapshot
Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior
inferredcites →https://doi.org/10.1007/bf00993309openalex-snapshot
C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993
inferredcites →https://doi.org/10.1007/s10462-022-10150-3openalex-snapshot
On the joint-effect of class imbalance and overlap: a critical review
inferredcites →https://doi.org/10.1007/s12027-020-00601-1openalex-snapshot
The European Labour Authority and rights-based labour mobility
inferredcites →https://doi.org/10.1016/j.aci.2018.08.003openalex-snapshot
Classification assessment methods
inferredcites →https://doi.org/10.1016/j.cose.2015.09.005openalex-snapshot
Intelligent financial fraud detection: A comprehensive review
inferredcites →https://doi.org/10.1016/j.dss.2021.113492openalex-snapshot
Data engineering for fraud detection
inferredcites →https://doi.org/10.1016/j.dss.2021.113664openalex-snapshot
Interpretable data science for decision making
inferredcites →https://doi.org/10.1016/j.eswa.2011.09.151openalex-snapshot
Designing of dynamic labor inspection system for construction industry
inferredcites →https://doi.org/10.1016/j.eswa.2012.01.204openalex-snapshot
Using data mining technique to enhance tax evasion detection performance
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.035openalex-snapshot
Learning from class-imbalanced data: Review of methods and applications
inferredcites →https://doi.org/10.1016/j.knosys.2019.105432openalex-snapshot
LAC: Library for associative classification
inferredcites →https://doi.org/10.1073/pnas.1900654116openalex-snapshot
Definitions, methods, and applications in interpretable machine learning
inferredcites →https://doi.org/10.1109/access.2020.2988359openalex-snapshot
An Explainable Machine Learning Framework for Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/icdm.2001.989541openalex-snapshot
CMAR: accurate and efficient classification based on multiple class-association rules
inferredcites →https://doi.org/10.1109/iisa50023.2020.9284414openalex-snapshot
Association Rules and Machine Learning for Enhancing Undeclared Work Detection
inferredcites →https://doi.org/10.1109/iisa52424.2021.9555543openalex-snapshot
Data Mining for Targeted Inspections Against Undeclared Work by Applying the CRISP-DM Methodology
inferredcites →https://doi.org/10.1109/iisa56318.2022.9904366openalex-snapshot
Undeclared Work Prediction Using Machine Learning: Dealing with the Class Imbalance and Class Overlap Problems
inferredcites →https://doi.org/10.1111/j.1467-8543.2008.00711.xopenalex-snapshot
Varieties of Undeclared Work in European Societies
inferredcites →https://doi.org/10.1137/1.9781611972733.40openalex-snapshot
CPAR: Classification based on Predictive Association Rules
inferredcites →https://doi.org/10.1142/s0219649214500270openalex-snapshot
Associative Classification Approaches: Review and Comparison
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/3359786openalex-snapshot
Techniques for interpretable machine learning
inferredcites →https://doi.org/10.3233/idt-210213openalex-snapshot
Addressing the issue of undeclared work – Part I: Applying associative classification per the CRISP-DM methodology
inferredcites →https://doi.org/10.3390/app10207013openalex-snapshot
Coverage-Based Classification Using Association Rule Mining
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-62316-5_1openalex-snapshot
Introduction to Advances in Artificial Intelligence-Empowered Decision Support Systems
inferred← citeshttps://doi.org/10.1007/978-3-032-30960-0_37openalex-snapshot
Class Overlap in Imbalanced Datasets: A Structured Review and Taxonomic Framework
inferred← citeshttps://doi.org/10.1109/aicecs63354.2024.10957672openalex-snapshot
A Systematic Survey on Machine Learning Classifier Systems to Address Class Imbalance
inferred← citeshttps://doi.org/10.1109/ict4da59526.2023.10302229openalex-snapshot
Detecting Fraud in Motor Insurance Claims Using XGBoost Algorithm with SMOTE
inferred← citeshttps://doi.org/10.1109/iisa59645.2023.10345943openalex-snapshot
Interpretable Machine Learning for Undeclared Work Prediction
inferred← citeshttps://doi.org/10.1109/iisa62523.2024.10786650openalex-snapshot
Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT
inferred← citeshttps://doi.org/10.1109/isiber68248.2026.11469952openalex-snapshot
Early Warning Prediction for Business Failure Using Digital Transaction Patterns: An Intelligent Framework for MSME Risk Assessment
inferred← citeshttps://doi.org/10.21511/afc.05(1).2024.05openalex-snapshot
Detecting tax evasion in the hospitality and tourism sector
inferred← citeshttps://doi.org/10.3390/electronics12214423openalex-snapshot
Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset
inferred← citeshttps://doi.org/10.3390/electronics12224663openalex-snapshot
Editorial Note to the Special Issue: “Trends and Applications in Information Systems and Technologies”
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Science and Advanced Computing — runs a structured editorial panel for researchers
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