Text classification using string kernels
The Journal of Machine Learning Research
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
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
A Multi-criteria Convex Quadratic Programming model for credit data analysis
Decision Support Systems
Neural nets versus conventional techniques in credit scoring in Egyptian banking
Expert Systems with Applications: An International Journal
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
A binary classification method for bankruptcy prediction
Expert Systems with Applications: An International Journal
Are we modelling the right thing? The impact of incorrect problem specification in credit scoring
Expert Systems with Applications: An International Journal
Credit Risk Assessment Model of Commercial Banks Based on Fuzzy Neural Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A data driven ensemble classifier for credit scoring analysis
Expert Systems with Applications: An International Journal
Least squares support vector machines ensemble models for credit scoring
Expert Systems with Applications: An International Journal
Multiple classifier application to credit risk assessment
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
Introduction to Machine Learning
Introduction to Machine Learning
Expert Systems with Applications: An International Journal
Multiple criteria optimization-based data mining methods and applications: a systematic survey
Knowledge and Information Systems
An empirical study of classification algorithm evaluation for financial risk prediction
Applied Soft Computing
Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey
Expert Systems with Applications: An International Journal
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning 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
Neural network metalearning for credit scoring
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
Many financial organizations such as banks and retailers use computational credit risk analysis (CRA) tools heavily due to recent financial crises and more strict regulations. This strategy enables them to manage their financial and operational risks within the pool of financial institutes. Machine learning algorithms especially binary classifiers are very popular for that purpose. In real-life applications such as CRA, feature selection algorithms are used to decrease data acquisition cost and to increase interpretability of the decision process. Using feature selection methods directly on CRA data sets may not help due to categorical variables such as marital status. Such features are usually are converted into binary features using 1-of-k encoding and eliminating a subset of features from a group does not help in terms of data collection cost or interpretability. In this study, we propose to use the probit classifier with a proper prior structure and multiple kernel learning with a proper kernel construction procedure to perform group-wise feature selection (i.e., eliminating a group of features together if they are not helpful). Experiments on two standard CRA data sets show the validity and effectiveness of the proposed binary classification algorithm variants.