Predictor Tree Learning Algorithm

From GM-RKB
Jump to: navigation, search

A predictor tree learning algorithm is an supervised eager model-based learning algorithm that produces a predictor tree.



References

2011

2005

  • (Rokach & Maimon, 2005) => Lior Rokach, and Oded Maimon. (2005). "Chapter 9. Decision Trees." In: Data Mining and Knowledge Discovery Handbook, Editors: Oded Z. Maimon, Lior Rokach ISBN 038725465X
    • 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 have dealt with 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 a top-down manner. The chapter suggests a unified algorithmic framework for presenting these algorithms and describes various splitting criteria and pruning methodologies.

2001

2008

  • (Wilson, 2008a) ⇒ Bill Wilson. (2008). "The Machine Learning Dictionary for COMP9414." University of New South Wales, Australia.
    • tree induction algorithm: This article describes the basic tree induction algorithm used by ID3 and successors. The basic idea is to pick an attribute A with values \(a_1, a_2, ..., a_r\), split the training instances into subsets \(S_{a1}, S_{a2}, ..., S_{ar}\) consisting of those instances that have the corresponding attribute value. Then if a subset has only instances in a single class, that part of the tree stops with a leaf node labelled with the single class. If not, then the subset is split again, recursively, using a different attribute. This leaves the question of how to choose the best attribute to split on at any branch node. This issue is handled in the article on splitting criterion in ID3.

1996

1993

1984


Personal tools
Namespaces

Variants
Views
Actions
Navigation
Tools