Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
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
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Ensembling neural networks: many could be better than all
Artificial Intelligence
Machine Learning
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A comparative assessment of ensemble learning for credit scoring
Expert Systems with Applications: An International Journal
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Two credit scoring models based on dual strategy ensemble trees
Knowledge-Based Systems
A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine
Expert Systems with Applications: An International Journal
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios
Knowledge-Based Systems
Exploring the behaviour of base classifiers in credit scoring ensembles
Expert Systems with Applications: An International Journal
A new ensemble method for gold mining problems: Predicting technology transfer
Electronic Commerce Research and Applications
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Forecasting corporate bankruptcy with an ensemble of classifiers
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
Partial Least Square Discriminant Analysis for bankruptcy prediction
Decision Support Systems
Hi-index | 12.07 |
Bankruptcy prediction is one of the major business classification topics. Both statistical approaches and artificial intelligence techniques have been explored for this topic. Most researchers compare the prediction performance using different techniques for a specific data set. However, there are no consistent results to show that one technique is better than the other. Different techniques have different advantages and disadvantages on different data sets. Recent studies suggest combining multiple classifiers may have a better performance. However, such an ensemble is usually not only to inherit advantages from the different classifiers but also suffers from disadvantages of those classifiers. In this paper, we propose a selective ensemble of three classifiers, i.e. the decision tree, the back propagation neural network and the support vector machine. Based on the expected probabilities of both bankruptcy and non-bankruptcy, this ensemble provides an approach which inherits advantages and avoids disadvantages of different classification techniques. Consequently, our selective ensemble performs better than other weighting or voting ensembles for bankruptcy prediction.