Variable Selection Algorithm in Classification Tree

• 발행기관: 한국자료분석학회
• 저자명: Jeong Yee Jang Kwang Mo Jeong
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.3 No.4, 361~369쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2001.12.30

영문 초록

A classification tree is a set of rules to classify and to predict the class of a target variable from one or more predictor variables. Main steps for constructing a tree consist of data partitioning, tree simplification, and model fitting. We especially focus on the topic of variable selection which is a very important step in constructing classification trees. In this paper we suggest an improved variable selection algorithm based on Kruskal-Wallis test which is a nonparametric competitor of F -test used in QUEST. We show through a Monte Carlo simulation that the proposed algorithm has some desirable properties in terms of bias and power when selecting a predictor variable.

목차

1. Introduction

2. Variable Selection Algorithms

3. Kruskal-Wallis Test Based Variable Selection

4. Conclusion

References