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
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
TL;DR: It is shown that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision.
Journal ArticleDOI
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Journal ArticleDOI
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
Alessandro Vinciarelli,Maja Pantic,Dirk Heylen,Catherine Pelachaud,Isabella Poggi,Francesca D'Errico,M. Schroeder +6 more
TL;DR: This is the first survey of the domain that jointly considers its three major aspects, namely, modeling, analysis, and synthesis of social behavior, which investigates laws and principles underlying social interaction, and explores approaches for automatic understanding of social exchanges recorded with different sensors.
Journal ArticleDOI
Fusion of Support Vector Machines for Classification of Multisensor Data
TL;DR: The proposed SVM-based fusion approach outperforms all other approaches and significantly improves the results of a single SVM, which is trained on the whole multisensor data set.
Journal ArticleDOI
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
TL;DR: This paper presents a unifying framework called Rain Forest for classification tree construction that separates the scalability aspects of algorithms for constructing a tree from the central features that determine the quality of the tree.
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.