New evolving ensemble classifier for handling concept drifting data streams
Kapil Keshao Wankhade, Snehlata Dongre, Ravindra C. Thool · 2012
Data streams mining have become a novel research topic of growing interest in knowledge discovery. The data streams which are generated from applications, such as network analysis, real time surveillance systems, sensor networks and financial generate huge data streams. These data streams consist of millions or billions of updates and must be processed to extract the useful information. Because of the high speed and huge size of data set in data streams, the traditional classification technologies are no longer applicable. In recent years a great deal of research has been done on this problem, most intends to efficiently solve the data streams mining problem with concept drift. This paper presents a novel approach for data stream classification which handles concept drift. This approach uses weighted majority approach with adaptive sliding window strategies. The experimental result shows that this novel approach works better than other methods.