A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
The Knowledge Engineering Review
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Financial failure prediction using efficiency as a predictor
Expert Systems with Applications: An International Journal
Construct support vector machine ensemble to detect traffic incident
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications: An International Journal
On strategies for imbalanced text classification using SVM: A comparative study
Decision Support Systems
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
Early warning of enterprise decline in a life cycle using neural networks and rough set theory
Expert Systems with Applications: An International Journal
Combining integrated sampling with SVM ensembles for learning from imbalanced datasets
Information Processing and Management: an International Journal
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Two credit scoring models based on dual strategy ensemble trees
Knowledge-Based Systems
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example
Knowledge-Based Systems
Preprocessing unbalanced data using support vector machine
Decision Support Systems
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Hybrid extreme rotation forest
Neural Networks
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Pre-warning of whether a corporate will fall into a decline stage in the near future is an emerging issue in financial management. Improper decision-making by firms incurs a higher possibility to cause financial crisis (distress) and deteriorates the soundness of financial markets. The aim of this study is to establish a novel prediction mechanism based on combining the sampling technique (synthetic minority over-sampling technique; SMOTE), feature selection ensemble (original, intersection, and union), extreme learning machine (ELM) ensemble and decision tree (DT). The proposed model - namely, the multiple extreme learning machines (MELMs) - shows promising performance under numerous assessing criteria, but one critical defect of the ensemble classifier is that it lacks comprehensibility. Thus, we perform a DT as the knowledge generator to extract the inherent information from the ensemble mechanism. This knowledge visualized process can assist decision makers in efficiently allocating limited financial resources and to help firms survive in an extremely competitive environment.