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ID3 algorithm

About: ID3 algorithm is a(n) research topic. Over the lifetime, 2309 publication(s) have been published within this topic receiving 115546 citation(s). The topic is also known as: Iterative Dichotomiser 3.

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Book
J. Ross Quinlan1Institutions (1)
15 Oct 1992-
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.

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Abstract: From the Publisher: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.

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21,396 citations


Journal ArticleDOI
25 Mar 1986-Machine Learning
Abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.

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16,062 citations


Book
01 Jan 1983-
Abstract: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

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14,724 citations


Proceedings ArticleDOI
Tianqi Chen1, Carlos Guestrin1Institutions (1)
13 Aug 2016-
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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10,428 citations


Journal ArticleDOI
S.R. Safavian1, David A. Landgrebe1Institutions (1)
01 Jun 1991-
TL;DR: 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.

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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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2,384 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
202121
202018
201942
201820
201783

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Topic's top 5 most impactful authors

Xizhao Wang

9 papers, 119 citations

Mikhail Moshkov

9 papers, 64 citations

Takashi Washio

6 papers, 92 citations

Igor Chikalov

6 papers, 75 citations

Hussein Almuallim

5 papers, 113 citations