Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data

Peerapon Vateekul; Miroslav Kubat

doi:10.1109/ICDMW.2009.94

Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data

Peerapon Vateekul, Miroslav Kubat

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

The paper focuses on automated categorization of text documents, each labeled with one or more classes and described by tens of thousands of features. The computational costs of induction in such domains are so high as almost to disqualify the use of decision trees; the reduction of these costs is thus an important research issue. Our own solution, FDT ("fast decision-tree induction"), uses a two-pronged strategy: (1) feature-set pre-selection, and (2) induction of several trees, each from a different data subset, with the combination of the results from multiple trees with a data-fusion technique tailored to domains with imbalanced classes.

Original language	English (US)
Title of host publication	ICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages	320-325
Number of pages	6
DOIs	https://doi.org/10.1109/ICDMW.2009.94
State	Published - Dec 1 2009
Event	2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States Duration: Dec 6 2009 → Dec 6 2009

Publication series

Name	ICDM Workshops 2009 - IEEE International Conference on Data Mining

Other

Other	2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
Country	United States
City	Miami, FL
Period	12/6/09 → 12/6/09

Keywords

Decision tree
Imbalanced classes
Large-scale data
Multi-label examples
Text categorization

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Vision and Pattern Recognition
Software

Access to Document

10.1109/ICDMW.2009.94

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Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data. / Vateekul, Peerapon; Kubat, Miroslav.

ICDM Workshops 2009 - IEEE International Conference on Data Mining. 2009. p. 320-325 5360425 (ICDM Workshops 2009 - IEEE International Conference on Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Vateekul, P & Kubat, M 2009, Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data. in ICDM Workshops 2009 - IEEE International Conference on Data Mining., 5360425, ICDM Workshops 2009 - IEEE International Conference on Data Mining, pp. 320-325, 2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009, Miami, FL, United States, 12/6/09. https://doi.org/10.1109/ICDMW.2009.94

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