Machine learning feature selection of financial data

다국어 초록 (Multilingual Abstract)

Global financial crisis has been occurred frequently in these days also na-
tions and companies pay attention to predict bankruptcy. In this thesis, we
discuss feature selection method to extract feature group that causes main
factors of making bankruptcy. We describe stepwise method and principal
component analysis method briefl
y and compare it to construct prediction
model. In addition, we try to analyze their performance and statistical mea-
surement which method is the most efficient to raw data. We deal with data
set of experiments which consist of 515 companies' financial statement in
1997 to build the model by using support vector machine.

목차 (Table of Contents)

• Abstract i
• 1 Introduction 1
• 2 Feature Selection Method 3
• 2.1 Stepwise method . . . . . . . . . . . . . . . . . . . . . . . . . 3
• 2.2 Principal component analysis . . . . . . . . . . . . . . . . . . 5
• 3 Data and experiment 10
• 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
• 3.2 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
• 4 Result 14
• 4.1 Stepwise method . . . . . . . . . . . . . . . . . . . . . . . . . 14
• 4.2 Principal component analysis . . . . . . . . . . . . . . . . . . 15
• 4.3 Comparing feature selection . . . . . . . . . . . . . . . . . . . 16
• 5 Conclusion 20
• Abstract (in Korean) 22