.png){kind=icon}
返回
Recurring and Novel Class Detection Using Class-Based Ensemble for Evolving Data Stream
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2015.2507123.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Streaming data is one of the attention receiving sources for concept-evolution studies. When a new class occurs in the data stream it can be considered as a new concept and so the concept-evolution. One attractive problem occurring in the concept-evolution studies is the recurring classes from our previous study. In data streams, a class can disappear and reappear after a while. Existing studies on data stream classification techniques either misclassify the recurring class or falsely identify the recurring classes as novel classes. Because of the misclassification or false novel classification, the error rates increases on those studies. In this paper we address the problem by defining a novel ensemble technique class-based ensemble which replaces the traditional chunk-based approach in order to detect the recurring classes. We discuss the details of two different approaches in class-based ensemble and explain and compare them in detail. Different than the previous studies in the field, we also prove the superiority of both class-based ensemble method over state-of-art techniques via empirical approach on a number of benchmark data sets including web comments as text mining challenge.
Keyword:
Database applications
clustering
classification
association rules
data mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏
分享收藏分享收藏
查看更多内容