Multilayer feedforward networks are universal approximators
Neural Networks
The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Credit risk analysis using a reliability-based neural network ensemble model
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Credit risk evaluation with least square support vector machine
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Neural network metalearning for credit scoring
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A bias-variance-complexity trade-off framework for complex system modeling
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
A new two-stage hybrid approach of credit risk in banking industry
Expert Systems with Applications: An International Journal
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A comparative assessment of ensemble learning for credit scoring
Expert Systems with Applications: An International Journal
Selecting useful features for personal credit risk analysis
International Journal of Business Information Systems
Expert Systems with Applications: An International Journal
Is grey relational analysis superior to the conventional techniques in predicting financial crisis?
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Credit risk evaluation using neural networks: Emotional versus conventional models
Applied Soft Computing
Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis
Expert Systems with Applications: An International Journal
A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine
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
Information Sciences: an International Journal
Application of polynomial projection ensembles to hedging crude oil commodity risk
Expert Systems with Applications: An International Journal
Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios
Knowledge-Based Systems
PISA: A framework for multiagent classification using argumentation
Data & Knowledge Engineering
Probabilistic Approaches For Credit Screening And Bankruptcy Prediction
International Journal of Intelligent Systems in Accounting and Finance Management
Expert Systems with Applications: An International Journal
Multi-agent based classification using argumentation from experience
Autonomous Agents and Multi-Agent Systems
Expert Systems with Applications: An International Journal
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the first stage, a bagging sampling approach is used to generate different training data subsets especially for data shortage. In the second stage, the different neural network models are created with different training subsets obtained from the previous stage. In the third stage, the generated neural network models are trained with different training datasets and accordingly the classification score and reliability value of neural classifier can be obtained. In the fourth stage, a decorrelation maximization algorithm is used to select the appropriate ensemble members. In the fifth stage, the reliability values of the selected neural network models (i.e., ensemble members) are scaled into a unit interval by logistic transformation. In the final stage, the selected neural network ensemble members are fused to obtain final classification result by means of reliability measurement. For illustration, two publicly available credit datasets are used to verify the effectiveness of the proposed multistage neural network ensemble model.