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
Activity recognition using hybrid generative/discriminative models on home environments using binary sensors.
TL;DR: This paper describes the use of two powerful machine learning schemes, ANN and SVM, within the framework of HMM (Hidden Markov Model), in order to tackle the task of activity recognition in a home setting and shows how the hybrid models achieve significantly better recognition performance.
Journal ArticleDOI
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time
TL;DR: This article reviews state-of-the-art wearable technologies that can be used for elderly care and discusses a series of considerations and future trends with regard to the construction of “smart clothing” system.
Proceedings ArticleDOI
Cross-feature analysis for detecting ad-hoc routing anomalies
TL;DR: A new data mining method is introduced that performs "cross-feature analysis" to capture the inter-feature correlation patterns in normal traffic to detect deviation (or anomalies) caused by attacks in MANET.
Journal ArticleDOI
Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds.
TL;DR: A newly developed algorithm for the generation of descriptors for noncongeneric compounds with traditional SAR approaches (molecular properties) and different machine learning algorithms for the induction of SARs from these descriptors are compared.
Journal ArticleDOI
A decision-tree-based symbolic rule induction system for text categorization
TL;DR: A decision-tree-based symbolic rule induction system for categorizing text documents automatically and a new method for converting a decision tree to a rule set that is simplified, but still logically equivalent to, the original tree is presented.
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.