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
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Posted Content
Predictive Monitoring of Business Processes
TL;DR: In this article, the authors present an approach to analyze event logs in order to predictively monitor business goals during business process execution and provide early advice so that users can steer ongoing process executions towards the achievement of business goals.
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A Streaming Parallel Decision Tree Algorithm
Yael Ben-Haim,Elad Tom-Tov +1 more
TL;DR: The algorithm is executed in a distributed environment and is especially designed for classifying large data sets and streaming data and is empirically shown to be as accurate as a standard decision tree classifier, while being scalable for processing of streaming data on multiple processors.
Book ChapterDOI
Preventing Student Dropout in Distance Learning Using Machine Learning Techniques
TL;DR: A number of experiments have taken place with data provided by the ‘informatics’ course of the Hellenic Open University and a quite interesting conclusion is that the Naive Bayes algorithm can be successfully used.
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Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data
TL;DR: A genetic programming algorithm and different data mining approaches are proposed for solving the problems of predicting student failure at school using real data about 670 high school students from Zacatecas, Mexico.
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Robust Process Discovery with Artificial Negative Events
TL;DR: This paper presents a configurable technique that deals with process discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs) that allows users to have a declarative control over the inductive bias and language bias.
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.