A Robust Ensemble Classification for Drifting Concepts

• 발행기관: 호서대학교 기초과학연구소
• 저자명: Kyupil Yeon
• 간행물 정보: 『기초과학연구 논문집』제22권 제1호, 65~75쪽, 전체 11쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2014.12.30

영문 초록

The concept drift phenomenon in a data stream mining refers to any circumstances under which the target concept changes over time abruptly or gradually. A machine learning algorithm should be equipped with some instruments in order to track such changing concepts whatever its extent or rate of drift is. In this article, we introduce a regression-based ensemble learning algorithm which is robust to outliers and fastly adapts to any concept drift as well. Fast adpatation to concept drift is materialized by regression-type combiners and robustness is facilitated into the algorithm by utilizing a 少一 likelihood estimation. Some simulation results with artificial data sets with concept drift verify that the proposed method shows a good classification accuracy under concept drift and various level of noises.

목차

Ⅰ. Introduction

Ⅱ. Ensemble Learning for Concept Drift

Ⅲ. Simulation Results

Ⅳ. Conclusions

Ⅴ. 참고문헌

키워드

concept drift ensemble learning 少一likelihood robustness