Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Deciding the financial health of dot-coms using rough sets
Information and Management
Expert Systems with Applications: An International Journal
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Predicting financial activity with evolutionary fuzzy case-based reasoning
Expert Systems with Applications: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
A genetic programming model for bankruptcy prediction: Empirical evidence from Iran
Expert Systems with Applications: An International Journal
Developing a business failure prediction model via RST, GRA and CBR
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Detection of financial statement fraud and feature selection using data mining techniques
Decision Support Systems
Principal component case-based reasoning ensemble for business failure prediction
Information and Management
Linear penalization support vector machines for feature selection
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine
Expert Systems with Applications: An International Journal
Financial distress prediction based on similarity weighted voting CBR
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
An application of support vector machine to companies' financial distress prediction
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Preprocessing unbalanced data using support vector machine
Decision Support Systems
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
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Financial risk forecasting (FRF) is an effective tool to help people forecast whether or not a company will fail in future. Among all techniques of FRF, the support vector machine (SVM) is the most newly developed, and one of the most accurate and effective techniques. This study is devoted to investigating an ensemble model of FRF by integrating bagging with an SVM to generate a data-driven SVM ensemble. Bagging is used to produce diverse training datasets on which multiple SVM classifiers are trained to make FRF for a target company. Simple voting is employed to produce a final decision from the SVM model committee. The empirical study has two objectives. One is to verify whether the data-driven SVM ensemble can produce a more dominating performance than the most frequently used techniques in the area of FRF, i.e. multivariate discriminant analysis, logistics regression and a single SVM. The other is to verify whether feature selection is necessary to help the SVM make more precise FRF, although the SVM can handle high-dimensional data. The results indicate that the data-driven SVM ensemble significantly improves the predictive ability of SVM-based FRF. Meanwhile, feature selection can effectively help the SVM achieve better predictive performance, which means that use of feature selection is necessary in SVM-based FRF. Copyright © 2012 John Wiley & Sons, Ltd.