A New Gradual Forgetting Approach for Mining Data Stream with Concept Drift

被引：1

作者：

Li, Yingrong ^{[1
]}

Wei, Yang ^{[1
]}

Kolesnikova, Anastasiya ^{[1
]}

Lee, Won Don ^{[1
]}

机构：

[1] Chungnam Natl Univ, Dept Comp Sci, Taejon, South Korea

来源：

ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 1 | 2008年

关键词：

data stream; data mining; concept drift;

D O I：

10.1109/ISISE.2008.255

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the real world concepts are often not stable but change with time. The underlying data distribution may change as well. The model built on old data will be necessarily updated. This problem is known as concept drift. Mining concept drifts is one of the most important fields in mining data stream. The paper presents a totally new gradual forgetting approach for mining concept-drift data stream. We firstly utilize UChoo to mine data stream with concept drift. UChoo defines a weight for each instance. The latest data which represents new data distribution has gradually higher weight than old data when time passing. The experiment result shows that the new method performs higher accuracy.

引用

页码：556 / 559

页数：4