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
Multi-relational data mining: an introduction
TL;DR: This article provides a brief introduction to MRDM, while the remainder of this special issue treats in detail advanced research topics at the frontiers of MRDM.
Journal ArticleDOI
On Similarity Preserving Feature Selection
TL;DR: It is shown, through theoretical analysis, that the proposed framework not only encompasses many widely used feature selection criteria, but also naturally overcomes their common weakness in handling feature redundancy.
Journal ArticleDOI
A Novel Bayes Model: Hidden Naive Bayes
TL;DR: This paper summarizes the existing improved algorithms and proposes a novel Bayes model: hidden naive Bayes (HNB), which significantly outperforms NB, SBC, NBTree, TAN, and AODE in terms of CLL and AUC.
Journal ArticleDOI
A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform
TL;DR: Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings and the proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
Journal ArticleDOI
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
TL;DR: In this paper, the spectral information provided by the Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, was tested with two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms.
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.