Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods

Expert Systems with Applications

Volumn 37, Issue 8, 2010, Pages 5895-5904

  Li, Hui ^a   Sun, Jie ^a   Wu, Jian ^a  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

Author keywords

Business failure prediction (BFP); Classification and regression tree (CART); Data mining

Indexed keywords

BUSINESS FAILURE; BUSINESS FAILURE PREDICTION; CHINESE LISTED COMPANIES; CLASSIFICATION AND REGRESSION TREE; CLASSIFICATION AND REGRESSION TREE (CART); CLASSIFICATION MINING; COMPARATIVE METHODS; CRITICAL TASKS; DATA MINING ALGORITHM; DATA MINING METHODS; DATA MINING TECHNIQUES; DATA SETS; ECONOMIC ENVIRONMENT; EMPIRICAL COMPARISON; EMPIRICAL RESULTS; FEATURE SUBSET; GOVERNMENT OFFICIALS; K-NEAREST NEIGHBORS; MULTIPLE DISCRIMINANT ANALYSIS; NON-LINEAR ESTIMATION; NON-PARAMETRIC; OPTIMAL ALGORITHM; PREDICTIVE PERFORMANCE; SIGNIFICANCE TEST; STEPWISE METHODS; STOCK EXCHANGE;

DATA MINING; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; FORECASTING; MINING; MOTION COMPENSATION; PLANT SHUTDOWNS; REGRESSION ANALYSIS; RESEARCH; SHAREHOLDERS; SOFTWARE ARCHITECTURE; SUPPORT VECTOR MACHINES;

TREES (MATHEMATICS);

EID: 77951206489     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.016     Document Type: Article

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