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
The WM method completed: a flexible fuzzy system approach to data mining
TL;DR: The Wang-Mendel method for generating fuzzy rules from data is enhanced to make it a comprehensive and flexible fuzzy system approach to data description and prediction, and an algorithm to optimize the fuzzy predictive models is proposed.
Dissertation
Text mining with information extraction
Un Yong Nahm,Raymond J. Mooney +1 more
TL;DR: Experimental results demonstrate that discovered patterns in extracted text can be used to effectively improve the underlying IE method, and an approach to using rules mined from extracted data to improve the accuracy of information extraction is presented.
Book ChapterDOI
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
TL;DR: The results are showing that dynamic reducts can help to extract laws from decision tables, e.g. market data, medical data, textures and handwritten digits.
Proceedings ArticleDOI
Using psycho-physiological measures to assess task difficulty in software development
TL;DR: A novel approach to classify the difficulty of code comprehension tasks using data from psycho-physiological sensors is investigated, bringing the community closer to a viable and reliable measure of task difficulty that could power the next generation of programming support tools.
Journal ArticleDOI
Sequential covering rule induction algorithm for variable consistency rough set approaches
TL;DR: This work presents a general rule induction algorithm based on sequential covering, suitable for variable consistency rough set approaches, and shows how to improve rule induction efficiency due to application of consistency measures with desirable monotonicity properties.
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.