Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Communications of the ACM
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
Machine Learning
Machine Learning
Data Mining and Knowledge Discovery
A perspective view and survey of meta-learning
Artificial Intelligence Review
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Introduction to the special issue on the fusion of domain knowledge with data for decision support
The Journal of Machine Learning Research
Introduction to the Special Issue on Meta-Learning
Machine Learning
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Camouflaged fraud detection in domains with complex relationships
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Large Scale Detection of Irregularities in Accounting Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Combining Information Extraction Systems Using Voting and Stacked Generalization
The Journal of Machine Learning Research
Adaptive Business Intelligence
Adaptive Business Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
Two-view feature generation model for semi-supervised learning
Proceedings of the 24th international conference on Machine learning
Journal of Management Information Systems
ACM Transactions on Information Systems (TOIS)
Current Trends in Fraud and its Detection
Information Security Journal: A Global Perspective
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
CONQUER: A Methodology for Context-Aware Query Processing on the World Wide Web
Information Systems Research
International Journal of Intelligent Systems in Accounting and Finance Management
A comparison of fraud cues and classification methods for fake escrow website detection
Information Technology and Management
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Constructive induction on decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Layered concept-learning and dynamically variable bias management
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Business Intelligence: Data Mining and Optimization for Decision Making
Business Intelligence: Data Mining and Optimization for Decision Making
Design and natural science research on information technology
Decision Support Systems
Detecting Management Fraud in Public Companies
Management Science
Semi-Supervised Learning
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
Design science in information systems research
MIS Quarterly
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Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators.