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Data Stream Classification using Random Forest and Very Fast Decision Tree
Objective: Data Stream Classification is a big problem. Ensemble based learning methods are used to tackle the data Stream classification problem. Method: Methods such as Random Forest and Very Fast Decision Tree (VFDT) for classification are used in the system. Findings: This hybrid approach is an Effective method for calculating hidden data and maintains accuracy in the large data set. It also calculates the neighbors between each pair of cases that can be used in clusters. It is used for finding unknown or (by scaling) gives informational views over the data. This hybrid approach achieves 85 % accuracy and result proves that the hybrid approach performs well when compared to other algorithms in terms of accuracy. This is the application where we can download data streams of any application at faster rate. Many methods are available to process data streams. But the proposed algorithm performs well when compared to other algorithms. Applications: Many real time data streams are downloaded and uploaded to test and train various Applications. Data streams are used in many applications such as medical applications and educational applications.
Classification, Data Stream, Random Forest, Very Fast Decision Tree (VFDT).
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