Generalized network modeling and diagnosis using financial ratios
Information Sciences: an International Journal - Special issue on expert systems
Neural Networks
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
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
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
Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
Information Sciences: an International Journal
Soft computing system for bank performance prediction
Applied Soft Computing
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Financial distress prediction by a radial basis function network with logit analysis learning
Computers & Mathematics with Applications
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
A case study on financial ratios via cross-graph quasi-bicliques
Information Sciences: an International Journal
Information Sciences: an International Journal
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
A survey of multiple classifier systems as hybrid systems
Information Fusion
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This paper presents novel neural network-genetic programming hybrids to predict the failure of dotcom companies. These hybrids comprise multilayer feed forward neural network (MLFF), probabilistic neural network (PNN), rough sets (RS) and genetic programming (GP) in a two-phase architecture. In each hybrid, one technique is used to perform feature selection in the first phase and another one is used as a classifier in the second phase. Further t-statistic and f-statistic are also used separately for feature selection in the first phase. In each of these cases, top 10 features are selected and fed to the classifier. Also, the NN-GP hybrids are compared with MLFF, PNN and GP in their stand-alone mode without feature selection. The dataset analyzed here is collected from Wharton Research Data Services (WRDS). It consists of 240 dotcom companies of which 120 are failed and 120 are healthy. Ten-fold cross-validation is performed throughout the study. Results in terms of average accuracy, average sensitivity, average specificity and area under the receiver operating characteristic curve (AUC) indicate that the GP outperformed all the techniques with or without feature selection. The superiority of GP-GP is demonstrated by t-test at 10% level of significance. Furthermore, the results are much better than those reported in previous studies on the same dataset.