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Journal ArticleDOI

A survey of decision tree classifier methodology

S.R. Safavian, +1 more
- Vol. 21, Iss: 3, pp 660-674
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TLDR
The subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed, and the relation between decision trees and neutral networks (NN) is also discussed.
Abstract
A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed. >

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MonographDOI

Planning Algorithms: Introductory Material

TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
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Ontology Matching

TL;DR: The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content.
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An assessment of support vector machines for land cover classification

TL;DR: An introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images are given.
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Decision tree classification of land cover from remotely sensed data

TL;DR: This work presents several types of decision tree classification algorithms and shows that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure.
Journal ArticleDOI

Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey

TL;DR: This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art.
References
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Journal ArticleDOI

Induction of Decision Trees

J. R. Quinlan
- 25 Mar 1986 - 
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book

Classification and regression trees

Leo Breiman
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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