Top-Down Induction of Decision Trees Classifiers -- A Survey (2002)
by
Lior Rokach
,
Oded Maimon
| Citations: | 7 - 2 self |
BibTeX
@MISC{Rokach02top-downinduction,
author = {Lior Rokach and Oded Maimon},
title = {Top-Down Induction of Decision Trees Classifiers -- A Survey},
year = {2002}
}
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Abstract
Decision Trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and provides profound descriptions of the various splitting criteria and pruning methodology.
