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 ArticleDOI
Simulation based optimization
TL;DR: This talk discusses the stochastic counterpart (sample path) method where a relatively large sample is generated and the expected value function is approximated by the corresponding average function, and the obtained approximation problem is solved by deterministic methods of nonlinear programming.
Proceedings ArticleDOI
Boosted Convolutional Neural Networks.
TL;DR: This work proposes a novel algorithm to incorporate boosting weights into the deep learning architecture based on least squares objective function and shows that it is possible to use networks of different structures within the proposed boosting framework and BoostCNN is able to select the best network structure in each iteration.
Proceedings ArticleDOI
An iterative method for multi-class cost-sensitive learning
TL;DR: This paper empirically evaluates the performance of the proposed method using benchmark data sets and proves that the method generally achieves better results than representative methods for cost-sensitive learning, in terms of predictive performance (cost minimization) and, in many cases, computational efficiency.
Proceedings ArticleDOI
A framework for dynamic energy efficiency and temperature management
TL;DR: In this paper, the authors proposed a framework that combines many energy management techniques and can activate them individually or in groups in a fine-grained manner according to a given policy.
Journal ArticleDOI
Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem
TL;DR: This study analyzes different classification algorithms that were employed to predict the creditworthiness of a bank's customers based on checking account information to determine a range of credit scores that could be implemented by a manager for risk management.
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.