Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
A case-based approach using inductive indexing for corporate bond rating
Decision Support Systems - Decision-making and E-commerce systems
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
A Decision Tree Scoring Model Based on Genetic Algorithm and K-Means Algorithm
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
A selective ensemble based on expected probabilities for bankruptcy prediction
Expert Systems with Applications: An International Journal
Feature selection in bankruptcy prediction
Knowledge-Based Systems
A new credit scoring method based on rough sets and decision tree
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Assessing print quality by machine in offset colour printing
Knowledge-Based Systems
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
An ensemble of decision cluster crotches for classification of high dimensional data
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
An improved boosting based on feature selection for corporate bankruptcy prediction
Expert Systems with Applications: An International Journal
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Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we propose two dual strategy ensemble trees: RS-Bagging DT and Bagging-RS DT, which are based on two ensemble strategies: bagging and random subspace, to reduce the influences of the noise data and the redundant attributes of data and to get the relatively higher classification accuracy. Two real world credit datasets are selected to demonstrate the effectiveness and feasibility of proposed methods. Experimental results reveal that single DT gets the lowest average accuracy among five single classifiers, i.e., Logistic Regression Analysis (LRA), Linear Discriminant Analysis (LDA), Multi-layer Perceptron (MLP) and Radial Basis Function Network (RBFN). Moreover, RS-Bagging DT and Bagging-RS DT get the better results than five single classifiers and four popular ensemble classifiers, i.e., Bagging DT, Random Subspace DT, Random Forest and Rotation Forest. The results show that RS-Bagging DT and Bagging-RS DT can be used as alternative techniques for credit scoring.