Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Computer
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetically Optimized Neural Network Classifiers for Bankruptcy Prediction- An Empirical Study
HICSS '96 Proceedings of the 29th Hawaii International Conference on System Sciences Volume 2: Decision Support and Knowledge-Based Systems
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Expert Systems with Applications: An International Journal
Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs
International Journal of Advanced Pervasive and Ubiquitous Computing
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Due to the radical changing of the global economy, a more precise forecasting of corporate financial distress helps provide important judgment principles to decision-makers. Although financial statements reflect a firm's business activities, it is very challenging to discover critical information from these statements. Applying machine learning algorithms can be demonstrated to improve forecasting accuracy in predicting corporate bankruptcy. In this paper, we introduce an evolutionary approach with modularized evaluation functions to forecast financial distress, which allows using any evolutionary algorithm to extract the set of critical financial ratios and integrates more evaluation function modules to achieve a better forecasting accuracy by assigning distinct weights. To achieve a more precise predicting accuracy, the undesirable forecasting results from some modules are weeded out, if their predicting accuracies are out of the allowable tolerance range as learned from our mechanism.