Forecasting with neural networks
Information and Management
Learning in the presence of concept drift and hidden contexts
Machine Learning
Self organizing neural networks for financial diagnosis
Decision Support Systems
Incremental Learning from Noisy Data
Machine Learning
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Computers and Operations Research
Deciding the financial health of dot-coms using rough sets
Information and Management
Expert Systems with Applications: An International Journal
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Financial distress early warning based on group decision making
Computers and Operations Research
A selective ensemble based on expected probabilities for bankruptcy prediction
Expert Systems with Applications: An International Journal
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
An application of support vector machine to companies' financial distress prediction
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Prior studies of financial distress prediction (FDP) all focus on static modeling and ignore whether the model is still suitable with time passing on. This paper devotes to the first investigation on what the concept of financial distress concept drift (FDCD) is, whether FDCD exists and how to dispose FDCD. We construct a dynamic FDP modeling based on instance selection for the disposal of FDCD. Dynamic FDP consists of instance selection, FDP modeling and future prediction. Instance selection methods including full memory window, no memory window, window of fixed size, window of adaptable size, and batch selection are used to tackle FDCD. For feature selection, we construct a wrapper by integrating forward and backward selections on Mahalanobis distance. Empirical results indicate that gradual and constant virtual concept drift does exist in FDP, and dynamic FDP models perform much better than static models. Meanwhile, window of fixed size and batch selection are more suitable for Chinese listed companies' dynamic FDP.