Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: concepts and techniques
Data mining: concepts and techniques
Selection of Training Data for Neural Networks by a Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Artificial neural networks with evolutionary instance selection for financial forecasting
Expert Systems with Applications: An International Journal
Constructing response model using ensemble based on feature subset selection
Expert Systems with Applications: An International Journal
Data mining techniques for cancer detection using serum proteomic profiling
Artificial Intelligence in Medicine
A new face authentication system for memory-constrained devices
IEEE Transactions on Consumer Electronics
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Computers and Operations Research
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One of the most important research issues in finance is building accurate corporate bankruptcy prediction models since they are essential for the risk management of financial institutions. Thus, researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques. Recently, support vector machines (SVMs) are becoming popular because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However, in order to use SVM, a user should determine several factors such as the parameters of a kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM. In this study, we propose a novel approach to enhance the prediction performance of SVM for the prediction of financial distress. Our suggestion is the simultaneous optimization of the feature selection and the instance selection as well as the parameters of a kernel function for SVM by using genetic algorithms (GAs). We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.