C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Consistency-based search in feature selection
Artificial Intelligence
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
Predictive automatic relevance determination by expectation propagation
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Efficient data reduction in multimedia data
Applied Intelligence
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Combined input variable selection and model complexity control for nonlinear regression
Pattern Recognition Letters
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural-network feature selector
IEEE Transactions on Neural Networks
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Automatic Relevance Determination for Identifying Thalamic Regions Implicated in Schizophrenia
IEEE Transactions on Neural Networks
Social media and online dating service providers: reexamining the new face of romance
International Journal of Business Information Systems
On the use of data filtering techniques for credit risk prediction with instance-based models
Expert Systems with Applications: An International Journal
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The recent credit crisis has renewed regulatory concerns of industrial interest in credit risk analysis. To reduce exposure to credit default, it thus becomes a crucial motive to select vital features to analyse the customer's credit profiles. This desired set of features can be generated through data mining techniques such as feature selection methods. However, each feature selection method has its advantages and limitations. In practice, using a single method inevitably introduces undesirable estimation bias. Instead, this paper proposes a bagging feature selection model, which is an ensemble learning approach, to identify the most significant features that determine the credit worthiness of customers. The experimental results demonstrate promising results using bagging feature selection model as compared to fundamental models for personal credit risk analysis.