An Efficient Indexing Structure for Group Models On Data Streams

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An Efficient Indexing Structure for Group Models On Data Streams

Journal Title: International journal of Emerging Trends in Science and Technology - Year 2016, Vol 3, Issue 2

Abstract

Group learning is a common tool for data stream classification, mainly because of its inherent advantages of handling huge volume of stream data and concept drifting. Have been mainly focused on building accurate group models from stream data. a linear scan of a huge number of base classifiers in the group during prediction incurs significant costs in response time, preventing group learning from being practical for many real world time critical data stream applications, such as web traffic monitoring spam detection, intrusion detection In these applications, data streams usually arrive at a speed of Giga byte per Seconds, and it is necessary to classify each stream record in a timely manner we propose a novel Ensemble tree indexing structure to organize all base classifiers in an grouped for fast prediction On Ensemble trees treat group as spatial databases and employee an Random tree like height balanced structure to minimize the expected prediction time of from linear to sub linear complexity. On the other hand, Ensemble trees can be automatically updated by continuously integrating new classifiers and other discarding outdated ones, well adapting to new trends and patterns underneath data streams

Authors and Affiliations

G. Sugantha

Keywords

EP ID EP242844
DOI -
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