An Extension of COSSO Algorithm by Combining Variables

• 발행기관: 한국자료분석학회
• 저자명: 최호식(Hosik Choi)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.9 No.5, 2117~2125쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2007.10.30

영문 초록

In the situation in which the number of variables(p) is comparatively larger than the number of sample(n) and there exist variables which have the same effects in the classifier, this paper presents a new variable selection algorithm extending COSSO (COmponent Selection and Smoothing Operator) by utilizing grouped variables. A grouped variable is composed of a component in the kernel expansion of a classifier. The construction of a kernel classifier is carried out by COSSO with the components made from grouped variables. From the results of simulations with synthetic and real data sets, the proposed method is verified to improve the performance of a kernel classifier and enhance the interpretability of the model.

목차

1. Introduction

2. ANOVA Decomposition and Component Selection

3. Proposed Algorithm

4. Numerical Studies

5. Discussion

References

키워드

kernel group variable COSSO classification