Ensemble Learning for Change-Point Prediction

被引：0

作者：

Hirade, Ryo ^{[1
]}

Yoshizumi, Takayuki ^{[1
]}

机构：

[1] IBM Res Tokyo, Tokyo, Japan

来源：

2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) | 2012年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1860 / 1863

页数：4

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