Optimal Partitioning for Classification Trees

• 발행기관: 한국자료분석학회
• 저자명: 안성진(Ahn, Sung Jin)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.7 No.3, 689~709쪽, 전체 21쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2005.06.30

영문 초록

This paper introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived based on the proportional-reduction-in-impurity(PRI) measure of association, which is proposed to extend the proportional-reduction-in-error(PRE) measure in the decision-theory context. For the method derivation, the PRI measure is analyzed to characterize its statistical distribution and association properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. The PRI criterion is applied to a numerical problem for illustration.

목차

1. Introduction

2. Proportional-reduction-in-impurity Measures of Association for Categorical Variables

3. Tree Construction Based on PRI Measure

4. Numerical Example

5. Concluding Remarks

References

키워드

classification tree k-means clustering loss function proportional reduction in impurity(PRI)