C4.5: programs for machine learning
C4.5: programs for machine learning
Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Decision Support Systems - Special issue: Data mining for financial decision making
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Bankruptcy prediction by generalized additive models: Research Articles
Applied Stochastic Models in Business and Industry
An agent-based decision support system for wholesale electricity market
Decision Support Systems
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Predicting going concern opinion with data mining
Decision Support Systems
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Credit risk measurement and early warning of SMEs: An empirical study of listed SMEs in China
Decision Support Systems
A comparative analysis of machine learning techniques for student retention management
Decision Support Systems
Rule extraction from support vector machines: A review
Neurocomputing
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Heterogeneous fuzzy logic networks: fundamentals and development studies
IEEE Transactions on Neural Networks
Forecasting corporate bankruptcy with an ensemble of classifiers
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Partial Least Square Discriminant Analysis for bankruptcy prediction
Decision Support Systems
Measuring firm performance using financial ratios: A decision tree approach
Expert Systems with Applications: An International Journal
A service oriented architecture to provide data mining services for non-expert data miners
Decision Support Systems
Expert Systems with Applications: An International Journal
An improved boosting based on feature selection for corporate bankruptcy prediction
Expert Systems with Applications: An International Journal
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
International Journal of Hybrid Intelligent Systems
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A great deal of research has been devoted to prediction of bankruptcy, to include application of data mining. Neural networks, support vector machines, and other algorithms often fit data well, but because of lack of comprehensibility, they are considered black box technologies. Conversely, decision trees are more comprehensible by human users. However, sometimes far too many rules result in another form of incomprehensibility. The number of rules obtained from decision tree algorithms can be controlled to some degree through setting different minimum support levels. This study applies a variety of data mining tools to bankruptcy data, with the purpose of comparing accuracy and number of rules. For this data, decision trees were found to be relatively more accurate compared to neural networks and support vector machines, but there were more rule nodes than desired. Adjustment of minimum support yielded more tractable rule sets.