Sciweavers

DIS
2006
Springer

Change Detection with Kalman Filter and CUSUM

14 years 10 months ago
Change Detection with Kalman Filter and CUSUM
Knowledge discovery systems are constrained by three main limited resources: time, memory and sample size. Sample size is traditionally the dominant limitation, but in many present-day data-mining applications the time and memory are the major limitations [6]. Several incremental learning algorithms have been proposed to deal with this limitations (e.g., [5, 12, 6]). However most learning algorithms, including the incremental, make the assumption that the examples are draw from stationary distribution [13]. The aim of this study is to present a detection system (DSKC) for regression problems. The system is modular and works as a post-processor of a regressor. It is composed by a regression predictor, a Kalman filter and a Cumulative Sum of Recursive Residual (CUSUM) change detector. The system continuously monitors the error of the regression model. A significant increase of the error is interpreted as a change in the distribution that generates the examples over time. When a change is...
Milton Severo, João Gama
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2006
Where DIS
Authors Milton Severo, João Gama
Comments (0)