Partially Supervised Classification for Early Concept Drift Detection

被引：0

作者：

Fuccellaro, Maxime ^{[1
]}

Simon, Laurent ^{[1
]}

Zemmari, Akka ^{[1
]}

机构：

[1] Univ Bordeaux, CNRS, Bordeaux INP, LaBRI,UMR 5800, Talence, France

来源：

2022 IEEE 34TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI | 2022年

关键词：

Concept Drift; Drift Detection; Semi Supervised; Data Streams; Real Drift;

D O I：

10.1109/ICTAI56018.2022.00035

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As more and more data is generated and stored, and as longer data streams become available, concept drift detection is becoming crucial for most real world applications. We introduce Partially Supervised Drift Detection, PSDD, a drift detection method based on Decision Trees that does not suppose any knowledge of true class labels during inference. Our approach works in any number of dimensions and is able to distinguish real from virtual drift. We successfully evaluated our method with well established datasets in the drift detection field.

引用

页码：190 / 196

页数：7

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