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Combining multiple class distribution modified subsamples in a single tree

delete2007-03-01
delete36
PRE
AI
J
Jesús M. Pérez *
J
Javier Muguerza
O
Olatz Arbelaitz
I
Ibai Gurrutxaga
J
J. Martín
DOI：10.1016/j.patrec.2006.08.013delete
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Abstract

Abstract

En 中文
This work describes the Consolidated Tree Construction (CTC) algorithm: a single tree is built based on a set of subsamples. This way the explaining capacity of the classifier is not lost even if many subsamples are used. We show how CTC algorithm can use undersampling to change class distribution without loss of information, building more accurate classifiers than C4.5. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
class distribution
decision tree
sampling
comprehensibility
C4.5
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Pattern Recognition Letters cover
Pattern Recognition Letters
IF:
3.3
Papers:
7.8K
Citations:
1.6W

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No organization information available