Cost-sensitive learning for imbalanced data streams

被引：23

作者：

Loezer, Lucas ^{[1
]}

Enembreck, Fabricio ^{[1
]}

Barddal, Jean Paul ^{[1
]}

Britto Jr, Alceu de Souza ^{[1
]}

机构：

[1] Pontificia Univ Catolica Parana, Grad Program Informat PPGIa, Curitiba, Parana, Brazil

来源：

PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20) | 2020年

关键词：

cost-sensitive; ensemble; data stream; imbalanced datasets; adaptive random forest; CLASSIFICATION;

D O I：

10.1145/3341105.3373949

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The data imbalance problem hampers the classification task. In streaming environments, this becomes even more cumbersome as the proportion of classes can vary over time. Approaches based on misclassification costs can be used to mitigate this problem. In this paper, we present the Cost-sensitive Adaptive Random Forest (CSARF) and compare it to the Adaptive Random Forest (ARF) and ARF with Resampling (ARF(RE)) in six real-world and six synthetic data sets with different class ratios. The empirical study analyzes two misclassification costs strategies of the CSARF and shows that the CSARF obtained statistically superior w.r.t. the average recall and average F1 when compared to ARF.

引用

页码：498 / 504

页数：7

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