The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
MAMBO: Discovering Association Rules Based on Conditional Independencies
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Bayesian Models for Early Warning of Bank Failures
Management Science
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Classifier ensembles: Select real-world applications
Information Fusion
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Hybrid mining approach in the design of credit scoring models
Expert Systems with Applications: An International Journal
Managing loan customers using misclassification patterns of credit scoring model
Expert Systems with Applications: An International Journal
Iterative bayesian network implementation by using annotated association rules
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Comparing classifiers and metaclassifiers
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
This study focuses on predicting whether a credit applicant can be categorized as good, bad or borderline from information initially supplied. This is essentially a classification task for credit scoring. Given its importance, many researchers have recently worked on an ensemble of classifiers. However, to the best of our knowledge, unrepresentative samples drastically reduce the accuracy of the deployment classifier. Few have attempted to preprocess the input samples into more homogeneous cluster groups and then fit the ensemble classifier accordingly. For this reason, we introduce the concept of class-wise classification as a preprocessing step in order to obtain an efficient ensemble classifier. This strategy would work better than a direct ensemble of classifiers without the preprocessing step. The proposed ensemble classifier is constructed by incorporating several data mining techniques, mainly involving optimal associate binning to discretize continuous values; neural network, support vector machine, and Bayesian network are used to augment the ensemble classifier. In particular, the Markov blanket concept of Bayesian network allows for a natural form of feature selection, which provides a basis for mining association rules. The learned knowledge is represented in multiple forms, including causal diagram and constrained association rules. The data driven nature of the proposed system distinguishes it from existing hybrid/ensemble credit scoring systems.