Mining concept drifts from data streams based on multi-classifiers - Details

Author：

Sun, Yue (Sun, Yue.) | Mao, Guojun (Mao, Guojun.) | Liu, Xu (Liu, Xu.) | Liu, Chunnian (Liu, Chunnian.)

Indexed by：

EI Scopus

Abstract：

Mining　concept　drifts　is　one　of　the　most　important　fields　in　mining　data　streams.　In　this　paper,　a　new　ensemble　algorithm　called　ICEA　is　proposed　for　mining　concept　drifts　from　data　streams,　which　uses　ensemble　multi-classifiers　to　detect　concept　changes　from　the　data　streams　in　an　incremental　way.　The　experimental　results　show　that　ICEA　algorithm　performs　higher　accuracy　and　better　adaptability　than　the　popular　methods　such　as　SEA　algorithm.　©　2007　IEEE.

Keyword：

Algorithms Data transfer Data structures Data mining

Author Community：

[ 1 ] [Sun, Yue]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, School of Computer Science, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Mao, Guojun]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, School of Computer Science, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Liu, Xu]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, School of Computer Science, Beijing University of Technology, Beijing 100022, China
[ 4 ] [Liu, Chunnian]Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, School of Computer Science, Beijing University of Technology, Beijing 100022, China