application of decision tree: bankruptcy prediction 2004/05/07

Application of Decision Tree: Bankruptcy Prediction

2004/05/07

Outline

• The prediction of bankruptcy • Model development

– Problem analysis– Feature selection– Data collection– Model building– Model evaluation

• Other applications

The prediction of bankruptcy

• Corporate bankruptcy brings with it losses to management, stockholders, employees, customers, and others.=>develop prediction models

• An unprecedented number of corporations are filing for bankruptcy in Korea during 1997 to 1998.

Model development (Problem analysis)

• Developing model, which– Predicts bankruptcy

– Generates understandable rules,

– Without much computation,

– Handles both continuous and categorical variables,

– Provides an indication of important variables.

=>Decision Tree

Model development(Feature selection)

Model development(Feature selection)

Model development(Data collection)

• Bankrupt firms were referred to as an act of filing a petition for bankruptcy reported by the KSE.

• 75 firms were identified.• After removing small firms and noise data, 29 ban

krupt firms remained.• 49 nonbankrupt were selected under certain criteri

a.• All financial data collected were gathered from the

KSE.

Model development(Model building)

• Decision tree induction approaches construct a decision tree using a training data set, where the tree is a simple recursive structure for representing a decision procedure in which a new case is assigned to one of the predefined classes. A nonterminal node in the tree represents a decision attribute value test, and a terminal node denotes a decision class.

Model development(Model building)

Model development(Model evaluation)

• Cross-validation approach– Divide whole data sets as training set and test set.

– Using training set to build model

– Using test set to evaluate model

• Jackknife approach– For n cases, using n-1 cases to build mode and using 1

case to evaluate model

Model development(Model evaluation)

Other application

• Widely applying on decision support problems, such as: – Information retrieval

• Ex. Text classification

– Business/Management• Ex. Credit prediction

– Healthcare/Biology• Ex. Health care fraud and abuse detection

– Computer science• Ex. Intrusion detection