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
Multivariate convex regression with adaptive partitioning
Lauren A. Hannah,David B. Dunson +1 more
TL;DR: This work introduces convex adaptive partitioning (CAP), which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions and demonstrates empirical performance by comparing the performance of CAP to other shape-constrained and unconstrained regression methods for predicting weekly wages and value function approximation for pricing American basket options.
Journal ArticleDOI
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
TL;DR: This paper examines a rough set FS technique which uses the information gathered from both the lower approximation dependency value and a distance metric which considers the number of objects in the boundary region and the distance of those objects from the lower approximations.
Journal ArticleDOI
Classifying imbalanced data sets using similarity based hierarchical decomposition
Cigdem Beyan,Robert B. Fisher +1 more
TL;DR: A new hierarchical decomposition method for imbalanced data sets which is different from previously proposed solutions to the class imbalance problem and does not require any data pre-processing step as many other solutions need.
Journal ArticleDOI
Protein classification with imbalanced data.
TL;DR: Generally, protein classification is a multi‐class classification problem and can be reduced to a set of binary classification problems, where one classifier is designed for each class, but in this case the number of proteins in one class is usually much smaller than that of the proteins outside the class.
Journal ArticleDOI
Creating Evolving User Behavior Profiles Automatically
TL;DR: This paper combines the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme and develops further the recursive formula of the potential of a data point to become a cluster center using cosine distance.
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.