2014Unpublished venueRequires access

Classification of Concept Drift Data Streams

E. Padmalatha, C. R. K. Reddy, B. Padmaja Rani

Open publisher page 3 citations

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.

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What this paper is about

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.

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Available 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.

Key concepts: Concept drift, Data stream mining, Computer science, Streaming data, Data stream, Class (philosophy), Key (lock), Data mining

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