Open Access
Programs for Machine Learning
Steven L. Salzberg,Alberto Segre +1 more
TLDR
In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.Abstract:
Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4.5, are probably the most popular in the machine learning community. These algorithms and variations on them have been the subject of numerous research papers since Quinlan introduced ID3. Until recently, most researchers looking for an introduction to decision trees turned to Quinlan's seminal 1986 Machine Learning journal article [Quinlan, 1986]. In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments. As such, this book will be a welcome addition to the library of many researchers and students.read more
Citations
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Journal ArticleDOI
Using Model Trees for Classification
TL;DR: Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
Journal ArticleDOI
Distributed learning in wireless sensor networks
TL;DR: In this article, the authors discuss nonparametric distributed learning in WSNs and discuss the challenges that wireless sensor networks pose for distributed learning, and research aimed at addressing these challenges is surveyed.
Journal ArticleDOI
Effective data mining using neural networks
Hongjun Lu,Rudy Setiono,Huan Liu +2 more
TL;DR: The paper presents an approach to discover symbolic classification rules using neural networks, and demonstrates the effectiveness of the proposed approach by the experimental results on a set of standard data mining test problems.
Journal ArticleDOI
Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines
TL;DR: This paper provides an overview of the recently proposed rule extraction techniques for SVMs and introduces two others taken from the artificial neural networks domain, being Trepan and G-REX, which rank at the top of comprehensible classification techniques.
Book
Sourcebook of parallel computing
Jack Dongarra,Ian Foster,Geoffrey C. Fox,William Gropp,Ken Kennedy,Linda Torczon,Andrew B. White +6 more
TL;DR: This chapter discusses parallelism in the context of scientific computing, which has applications in environment and energy, problem-Solving Environments, and more.
References
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Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
Induction of Decision Trees
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
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.
Journal ArticleDOI
An Empirical Comparison of Pruning Methods for Decision Tree Induction
TL;DR: This paper compares five methods for pruning decision trees, developed from sets of examples, and shows that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems.
Book ChapterDOI
Unknown attribute values in induction
TL;DR: This paper compares the effectiveness of several approaches to the development and use of decision tree classifiers as measured by their performance on a collection of datasets.