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
Comparing data mining methods with logistic regression in childhood obesity prediction
TL;DR: It has been shown that incorporation of non-linear interactions could be important in epidemiological prediction, and that data mining techniques are becoming sufficiently well established to offer the medical research community a valid alternative to logistic regression.
Journal ArticleDOI
Pareto-optimal patterns in logical analysis of data
TL;DR: This paper model various such suitability criteria as partial preorders defined on the set of patterns, and introduces three such preferences, and describes patterns which are Pareto-optimal with respect to any one of them, or to certain combinations of them.
Journal ArticleDOI
Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data.
TL;DR: The use of domain knowledge and spatial data is used to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk.
Book ChapterDOI
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
TL;DR: In this paper, the authors compared the learning performance of two genetic-based learning systems, XCS and GALE, with six well-known learning algorithms, coming from instance based learning, decision tree induction, rule-learning, statistical modeling and support vector machines.
Data Mining using Genetic Programming : Classification and Symbolic Regression
TL;DR: The work in this thesis has been carried out under the auspices of the research school IPA (Institute for Programming research and Algorithmics)
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.