Classification of Concept Drift Data Streams
E. Padmalatha, C. R. K. Reddy, B. Padmaja Rani
Abstract
E. Padmalatha, C. R. K. Reddy, B. Padmaja Rani
Abstract
Concept drift has been a very important concept in the realm of data streams. Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. Concept drift occurs when a set of examples has legitimate class labels at one time and has different legitimate labels at another time. This paper provides a comprehensive overview of existing concept -evolution in concept drifting techniques along different dimensions and it provides lucid vision about the ensemble's behavior when dealing with concept drifts. Key words:data stream,ensemble, class label,concept drift.
OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Concept drift has been a very important concept in the realm of data streams. Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. Concept drift occurs when a set of examples has legitimate class labels at one time and has different legitimate labels at another time. This paper provides a comprehensive overview of existing concept -evolution in concept drifting techniques along different dimensions and it provides lucid vision about the ensemble's behavior when dealing with concept drifts. Key words:data stream,ensemble, class label,concept drift.
Key concepts: Concept drift, Data stream mining, Computer science, Streaming data, Data stream, Class (philosophy), Key (lock), Data mining