Title :
Ensemble learning for change-point prediction
Author :
Hirade, R. ; Yoshizumi, Tomo
Author_Institution :
IBM Res., Tokyo, Japan
Abstract :
In this paper, we propose a novel algorithm for the problem of predicting change-points. We assume that the causes for change-points can be characterized by the time interval between a change-point and its symptom. Based on this assumption, we first generate weak classifiers for capturing each characteristic, and then build an ensemble classifier with the weak classifiers. Experimental results show our algorithm improves the F-measure by 11% in the best case.
Keywords :
learning (artificial intelligence); pattern classification; prediction theory; F-measure; change-point prediction; ensemble classifier; ensemble learning; time interval characterization; weak classifiers generation; Data mining; Data models; Decision trees; Prediction algorithms; Sensors; Time series analysis; Vectors;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4
