Comprehensive Study on Techniques of Incremental Learning With Decision Trees for Streamed Data

Provided by: International Journal of Engineering and Advanced Technology (IJEAT)
Topic: Data Management
Format: PDF
Incremental learning is an approach to deal with the classification task when datasets are too large or when new examples can arrive at any time. Data streams are inherently time-varying and exhibit various types of dynamics. There are some problems in data stream mining like class imbalance, concept drift, arrival of a novel class, etc. This paper focuses on the problem of concept drift. The presence of concept drift in the data significantly influences the accuracy of the learner, thus efficient handling of non-stationary environment is an important problem.

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