Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Learning distributions by their density levels: a paradigm for learning without a teacher
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improving the manufacturability of electronic designs
IEEE Spectrum
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Short communication: Data mining based intelligent analysis of threatening e-mail
Knowledge-Based Systems
A classification technique based on radial basis function neural networks
Advances in Engineering Software
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
A probabilistic approach to fraud detection in telecommunications
Knowledge-Based Systems
Multivariate convex support vector regression with semidefinite programming
Knowledge-Based Systems
WSEAS Transactions on Information Science and Applications
Game team balancing by using particle swarm optimization
Knowledge-Based Systems
Granular support vector machine based on mixed measure
Neurocomputing
A regularization for the projection twin support vector machine
Knowledge-Based Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
A proximal classifier with consistency
Knowledge-Based Systems
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Detecting fraudulent financial statements (FFS) is critical in order to protect the global financial market. In recent years, FFS have begun to appear and continue to grow rapidly, which has shocked the confidence of investors and threatened the economics of entire countries. While auditors are the last line of defense to detect FFS, many auditors lack the experience and expertise to deal with the related risks. This study introduces a support vector machine-based fraud warning (SVMFW) model to reduce these risks. The model integrates sequential forward selection (SFS), support vector machine (SVM), and a classification and regression tree (CART). SFS is employed to overcome information overload problems, and the SVM technique is then used to assess the likelihood of FFS. To select the parameters of SVM models, particle swarm optimization (PSO) is applied. Finally, CART is employed to enable auditors to increase substantive testing during their audit procedures by adopting reliable, easy-to-grasp decision rules. The experiment results show that the SVMFW model can reduce unnecessary information, satisfactorily detect FFS, and provide directions for properly allocating audit resources in limited audits. The model is a promising alternative for detecting FFS caused by top management, and it can assist in both taxation and the banking system.