e-journal

E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams

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Ensemble learning is a common tool for data stream classification, mainly because of its inherent advantages of handling large volumes of stream data and concept drifting. Previous studies, to date, have been primarily focused on building accurate ensemble models from stream data. However, a linear scan of a large number of base classifiers in the ensemble during prediction incurs significant costs in response time, preventing ensemble learning from being practical for many real-world timecritical data stream applications, such as Web traffic stream monitoring, spam detection, and intrusion detection. In these
applications, data streams usually arrive at a speed of GB/second, and it is necessary to classify each stream record in a timely manner. To address this problem, we propose a novel Ensemble-tree (E-tree for short) indexing structure to organize all base classifiers in an ensemble for fast prediction. On one hand, E-trees treat ensembles as spatial databases and employ an R-tree like height-balanced structure to reduce the expected prediction time from linear to sub-linear complexity. On the other hand, E-trees
can be automatically updated by continuously integrating new classifiers and discarding outdated ones, well adapting to new trends and patterns underneath data streams. Theoretical analysis and empirical studies on both synthetic and real-world data streams demonstrate the performance of our approach.
Index Terms—Stream data mining, classification, ensemble learning, spatial indexing, concept drifting

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 27, NO. 2, FEBRUARY 2015

Bahasa

English

ISBN/ISSN

1041-4347

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 27, NO. 2, FEBRUARY 2015

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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