Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
The design and validation of a hybrid information system for the auditor's going concern decision
Journal of Management Information Systems - Special section: Managing virtual workplaces and teleworking with information technology
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Hybrid mining approach in the design of credit scoring models
Expert Systems with Applications: An International Journal
Failure prediction with self organizing maps
Expert Systems with Applications: An International Journal
An artificial immune classifier for credit scoring analysis
Applied Soft Computing
Credit rating using a hybrid voting ensemble
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by 'Classification+Classification', 'Classification+Clustering', 'Clustering+Classification', and 'Clustering+Clustering' techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the 'Classification+Classification' hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.