Paper Info
Title
Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction
Abstract
This paper proposes a novel method of corporate financial risk prediction (FRP) modeling called the adaptive and dynamic ensemble (ADE) of support vector machine (SVM) (ADE-SVM), which integrates the inflow of new data batches for FRP with the process of time. Namely, the characteristic change of corporate financial distress hidden in the data flow is considered as the concept drift of financial distress, and it is handled by ADE-SVM that keeps updating in time. Using the criteria of predictive ability and classifier diversity, the SVM ensemble is dynamically constructed by adaptively selecting the current base SVMs from candidate ones. The candidate SVMs are incrementally updated by considering the newest data batch at each new current time point. The results of the base SVMs are dynamically weighted by their validation accuracies on the latest data batch to generate the final prediction. Experiments were carried out on real-world data sets with current data for training and future data for testing. The results show that ADE-SVM overall outperforms the other three traditional dynamic modeling methods, particularly for harder FRP task with more insufficient information and more obvious concept drift.
Year
DOI
Venue
2013
10.1109/TSMCA.2012.2224338
IEEE T. Systems, Man, and Cybernetics: Systems
Keywords
Field
DocType
Support vector machines,Training data,Accuracy,Data models,Adaptation models,Predictive models,Companies
Data integration,Financial risk,Data mining,Data set,Computer science,Support vector machine,Concept drift,System dynamics,Artificial intelligence,Classifier (linguistics),Machine learning,Data flow diagram
Journal
Volume
Issue
ISSN
43
4
2168-2216
Citations 
PageRank 
References 
14
0.58
51
Authors
3
Name
Order
Citations
PageRank
Jie Sun125711.06
Hui Li229012.71
Hojjat Adeli32150148.37