A novel support vector machine metamodel for business risk identification

Authors:
Kin Keung Lai;Lean Yu;Wei Huang;Shouyang Wang
Affiliations:
College of Business Administration, Hunan University, Changsha, China;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong and Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beiji ...;School of Management, Huazhong University of Science and Technology, Wuhan, China;College of Business Administration, Hunan University, Changsha, China and Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 4
Cited 4

The nature of statistical learning theory

The nature of statistical learning theory
Bagging predictors

Machine Learning
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms

Expert Systems with Applications: An International Journal
A bias-variance-complexity trade-off framework for complex system modeling

ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I

An Evolutionary Programming Based SVM Ensemble Model for Corporate Failure Prediction

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
An Intelligent CRM System for Identifying High-Risk Customers: An Ensemble Data Mining Approach

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Support vector machine based multiagent ensemble learning for credit risk evaluation

Expert Systems with Applications: An International Journal
Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM)

CIT'09 Proceedings of the 3rd International Conference on Communications and information technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, support vector machine (SVM) is used as a metamodeling technique to design a business risk identification system. First of all, a bagging sampling technique is used to generate different training sets. Based on the different training sets, different SVM models with different parameters, i.e., base models, are then trained to formulate different classifiers. Finally, a SVM-based metamodel (i.e., metaclassifier) can be produced by learning from all base models. For illustration the proposed metamodel is applied to a real-world business insolvency risk classification problem.