A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine

Authors:
Gang Wang;Jian Ma
Affiliations:
School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui, ...;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 16
Cited 3

The Strength of Weak Learnability

Machine Learning
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Bagging predictors

Machine Learning
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
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
Random Forests

Machine Learning
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
Credit scoring with a data mining approach based on support vector machines

Expert Systems with Applications: An International Journal
Credit risk assessment with a multistage neural network ensemble learning approach

Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring

Expert Systems with Applications: An International Journal
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring

Expert Systems with Applications: An International Journal
A selective ensemble based on expected probabilities for bankruptcy prediction

Expert Systems with Applications: An International Journal
A comparative assessment of ensemble learning for credit scoring

Expert Systems with Applications: An International Journal

Credit rating using a hybrid voting ensemble

SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection

International Journal of Intelligent Systems in Accounting and Finance Management
A survey of multiple classifier systems as hybrid systems

Information Fusion

Quantified Score

Hi-index	12.05

Visualization

Abstract

Enterprise credit risk assessment has long been regarded as a critical topic and many statistical and intelligent methods have been explored for this issue. However there are no consistent conclusions on which methods are better. Recent researches suggest combining multiple classifiers, i.e., ensemble learning, may have a better performance. In this paper, we propose a new hybrid ensemble approach, called RSB-SVM, which is based on two popular ensemble strategies, i.e., bagging and random subspace and uses Support Vector Machine (SVM) as base learner. As there are two different factors, i.e., bootstrap selection of instances and random selection of features, encouraging diversity in RSB-SVM, it would be advantageous to get better performance. The enterprise credit risk dataset, which includes 239 companies' financial records and is collected by the Industrial and Commercial Bank of China, is selected to demonstrate the effectiveness and feasibility of proposed method. Experimental results reveal that RSB-SVM can be used as an alternative method for enterprise credit risk assessment.