Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Bayesian Models for Early Warning of Bank Failures
Management Science
Neural and Wavelet Network Models for Financial Distress Classification
Data Mining and Knowledge Discovery
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Fuzzy functions with support vector machines
Information Sciences: an International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
A rough margin based support vector machine
Information Sciences: an International Journal
Information Sciences: an International Journal
A practical approach to credit scoring
Expert Systems with Applications: An International Journal
Support vector regression from simulation data and few experimental samples
Information Sciences: an International Journal
On-line fuzzy modeling via clustering and support vector machines
Information Sciences: 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
Developing a business failure prediction model via RST, GRA and CBR
Expert Systems with Applications: An International Journal
Associated evolution of a support vector machine-based classifier for pedestrian detection
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Alternative diagnosis of corporate bankruptcy: A neuro fuzzy approach
Expert Systems with Applications: An International Journal
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Ensemble with neural networks for bankruptcy prediction
Expert Systems with Applications: An International Journal
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
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
Hi-index | 12.05 |
Business failure prediction (BFP) is an effective tool to help financial institutions and relevant people to make the right decision in investments, especially in the current competitive environment. This topic belongs to a classification-type task, one of whose aims is to generate more accurate hit ratios. Support vector machine (SVM) is a statistical learning technique, whose advantage is its high generalization performance. The objective of this context is threefold. Firstly, SVM is used to predict business failure by utilizing a straightforward wrapper approach to help the model produce more accurate prediction. The wrapper approach is fulfilled by employing a forward feature selection method, composed of feature ranking and feature selection. Meanwhile, this work attempts to investigate the feasibility of using linear SVMs to select features for all SVMs in the wrapper since non-linear SVMs yield to over-fit the data. Finally, a robust re-sampling approach is used to evaluate model performances for the task of BFP in China. In the empirical research, performances of linear SVM, polynomial SVM, Gaussian SVM, and sigmoid SVM with the best filter of stepwise MDA, and wrappers respectively using linear SVM and non-linear SVMs as evaluating functions are to be compared. The results indicate that the non-linear SVM with radial basis function kernel and features selected by linear SVM compare significantly superiorly to all the other SVMs. Meanwhile, all SVMs with features selected by linear SVM produce at least as good performances as SVMs with other optimal features.