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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Proceedings Article
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
TL;DR: This work uses techniques from statistical pattern recognition to efficiently separate the feature words or discriminants from the noise words at each node of the taxonomy, and builds a multi-level classifier that has a small model size and is very fast.
Proceedings Article
Learning a Rare Event Detection Cascade by Direct Feature Selection
TL;DR: A novel cascade learning algorithm based on forward feature selection which is two orders of magnitude faster than the Viola-Jones approach and yields classifiers of equivalent quality could be used for more demanding classification tasks, such as on-line learning.
Journal ArticleDOI
A new dependency and correlation analysis for features
TL;DR: The results show that, using the new decision dependent correlation metric, the data mining approach can efficiently detect rare network attacks such as User to Root (U2R) and Remote to Local (R2L) attacks.
Journal ArticleDOI
An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems
TL;DR: In this study neural network and genetic algorithm fuzzy rule induction systems have been developed and applied to three classification problems and it is indicated that the genetic/fuzzy approach compares more than favourably with the neuro/ fuzzy and rough set approaches.
Journal ArticleDOI
Object-Based Image Classification of Summer Crops with Machine Learning Methods
José M. Peña,Pedro Antonio Gutiérrez,César Hervás-Martínez,Johan Six,Richard E. Plant,Francisca López-Granados +5 more
TL;DR: The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.
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.