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
More filters
Journal ArticleDOI
Graph-based data mining
Diane J. Cook,Lawrence B. Holder +1 more
TL;DR: Subdue as mentioned in this paper performs unsupervised pattern discovery and supervised concept learning from examples using graph-based data mining techniques for structural databases, which can be applied to large structural databases.
Journal ArticleDOI
A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.
TL;DR: This work presents a comparative study on six feature selection heuristics by applying them to two sets of data, which are gene expression profiles from Acute Lymphoblastic Leukemia and proteomic patterns from ovarian cancer patients.
Proceedings ArticleDOI
Mining product reputations on the Web
TL;DR: A new framework for mining product reputations on the Internet is presented, which offers a drastic reduction in the overall cost of reputation analysis over that of conventional survey approaches and supports the discovery of knowledge from the pool of opinions on the web.
Book ChapterDOI
Centroid-Based Document Classification: Analysis and Experimental Results
Eui-Hong Han,George Karypis +1 more
TL;DR: The authors' experiments show that this centroidbased classifier consistently and substantially outperforms other algorithms such as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets.
Proceedings ArticleDOI
KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor
TL;DR: KeyGraph presents an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural-language processing tools or a document corpus, based on the segmentation of a graph.
References
More filters
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.