Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Self organizing neural networks for financial diagnosis
Decision Support Systems
Machine Learning
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support
Information Sciences—Informatics and Computer Science: An International Journal
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Neural and Wavelet Network Models for Financial Distress Classification
Data Mining and Knowledge Discovery
Deciding the financial health of dot-coms using rough sets
Information and Management
Soft computing system for bank performance prediction
Applied Soft Computing
Financial distress prediction by a radial basis function network with logit analysis learning
Computers & Mathematics with Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Parallel consensual neural networks
IEEE Transactions on Neural Networks
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
Using partial least squares and support vector machines for bankruptcy prediction
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
Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios
Knowledge-Based Systems
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Financial ratio selection for business crisis prediction
Expert Systems with Applications: An International Journal
Novel feature selection methods to financial distress prediction
Expert Systems with Applications: An International Journal
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This paper presents a novel hybrid intelligent system in the framework of soft computing to predict the failure of dotcom companies. The hybrid intelligent system comprises the techniques such as a Multilayer Perceptrons (MLP), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Classification and Regression Trees (CART). The dataset collected from Wharton Research Data Services (WRDS) consists of 240 dotcom companies (also known as click-and-mortar companies), of which 120 are failed and 120 are healthy. Ten-fold cross validation is performed on the data set for all the techniques considered in their stand-alone mode. Further, two hybrid techniques viz., ensembling and boosting are employed to improve the accuracies. Moreover, t-statistic is performed on the dataset for feature selection purpose and the reduced feature subset with 10 features is extracted. The reduced feature subset is tested with all the techniques and then ensembling and boosting is also done for the reduced feature subset. Results supported by Receiver Operating Characteristic (ROC) curve indicate that the important features extracted by the t-statistic based feature subset selection yielded very high accuracies for all the techniques. Furthermore, the results are superior to those reported in previous studies on the same data set.