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THE DYNAMIC ENSEMBLE GENERATION AND CLASSIFICATION ALGORITHM FOR MINING HIGH SPEED DATA STREAMS

Journal: International Journal of Computer Science and Mobile Applications IJCSMA (Vol.6, No. 1)

Publication Date:

Authors : ; ;

Page : 81-94

Keywords : Ensemble Classification; Classification; Data Streams; High speed Data streams; Data mining;

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Abstract

The class evolution, the occurrence of class appearance and disappearance, is an important research topic for mining high speed data streams. All previous studies implicitly observe class evolution as a temporary change, which is not true for many real-world problems. This paper concerns the situation where classes emerge or disappear gradually. A class-based ensemble method viz. The CBCE (Class-Based ensemble for Class-Evolution) is projected by keeping a base learner for every class and vigorously updating the base learners with new data, it can quickly adjust to class development. A novel under-sampling method for the base learners is also intended to handle the dynamic class-unbalance problem caused by the gradual evolution of classes. The data-based studies reveal the effectiveness of CBCE in a variety of class evolution state in comparison to existing class evolution adaptation methods.

Last modified: 2018-02-09 18:29:11