Concept Drift Detection using Supervised Bivariate Grids
被引:0
|
作者:
Salperwyck, Christophe
论文数: 0引用数: 0
h-index: 0
机构:
EDF R&D, 1 Ave Gen Gaulle, F-92140 Clamart, FranceEDF R&D, 1 Ave Gen Gaulle, F-92140 Clamart, France
Salperwyck, Christophe
[1
]
Boulle, Marc
论文数: 0引用数: 0
h-index: 0
机构:
Orange Labs, F-22300 Lannion, FranceEDF R&D, 1 Ave Gen Gaulle, F-92140 Clamart, France
Boulle, Marc
[2
]
Lemaire, Vincent
论文数: 0引用数: 0
h-index: 0
机构:
Orange Labs, F-22300 Lannion, FranceEDF R&D, 1 Ave Gen Gaulle, F-92140 Clamart, France
Lemaire, Vincent
[2
]
机构:
[1] EDF R&D, 1 Ave Gen Gaulle, F-92140 Clamart, France
[2] Orange Labs, F-22300 Lannion, France
来源:
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
|
2015年
关键词:
BAYES;
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:
We present an on-line method for concept change detection on labeled data streams. Our detection method uses a bivariate supervised criterion to determine if the data in two windows come from the same distribution. Our method has no assumption neither on data distribution nor on change type. It has the ability to detect changes of different kinds (mean, variance ... ). Experiments show that our method performs better than well-known methods from the literature. Additionally, except from the window sizes, no user parameter is required in our method.