The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Models for Early Warning of Bank Failures
Management Science
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
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
Top 10 algorithms in data mining
Knowledge and Information Systems
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
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
Rule effectiveness in rule-based systems: A credit scoring case study
Expert Systems with Applications: An International Journal
Classification of functional data: A segmentation approach
Computational Statistics & Data Analysis
A hybrid financial analysis model for business failure prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A practical approach to credit scoring
Expert Systems with Applications: An International Journal
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
A genetic programming model for bankruptcy prediction: Empirical evidence from Iran
Expert Systems with Applications: An International Journal
Developing a business failure prediction model via RST, GRA and CBR
Expert Systems with Applications: An International Journal
A binary classification method for bankruptcy prediction
Expert Systems with Applications: An International Journal
A selective ensemble based on expected probabilities for bankruptcy prediction
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
Beyond business failure prediction
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Financial distress prediction based on similarity weighted voting CBR
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Expert Systems with Applications: An International Journal
Search and analysis of bankruptcy cause by classification network
MEDI'11 Proceedings of the First international conference on Model and data engineering
CART-based selection of bankruptcy predictors for the logit model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Predicting business failure is a very critical task for government officials, stock holders, managers, employees, investors and researchers, especially in nowadays competitive economic environment. Several top 10 data mining methods have become very popular alternatives in business failure prediction (BFP), e.g., support vector machine and k nearest neighbor. In comparison with the other classification mining methods, advantages of classification and regression tree (CART) methods include: simplicity of results, easy implementation, nonlinear estimation, being non-parametric, accuracy and stable. However, there are seldom researches in the area of BFP that witness the applicability of CART, another method among the top 10 algorithms in data mining. The aim of this research is to explore the performance of BFP using the commonly discussed data mining technique of CART. To demonstrate the effectiveness of BFP using CART, business failure predicting tasks were performed on the data set collected from companies listed in the Shanghai Stock Exchange and Shenzhen Stock Exchange. Thirty times' hold-out method was employed as the assessment, and the two commonly used methods in the top 10 data mining algorithms, i.e., support vector machine and k nearest neighbor, and the two baseline benchmark methods from statistic area, i.e., multiple discriminant analysis (MDA) and logistics regression, were employed as comparative methods. For comparative methods, stepwise method of MDA was employed to select optimal feature subset. Empirical results indicated that the optimal algorithm of CART outperforms all the comparative methods in terms of predictive performance and significance test in short-term BFP of Chinese listed companies.