• Publications
  • Influence
C4.5: Programs for Machine Learning
TLDR
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Expand
Induction of Decision Trees
TLDR
This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, which is described in detail. Expand
Learning With Continuous Classes
TLDR
This paper describes a new system, m5, that constructs tree-based piecewise linear models, and four case studies are presented in which m5 is compared to other methods. Expand
Top 10 algorithms in data mining
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN,Expand
Learning Logical Definitions from Relations
This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends themExpand
Improved Use of Continuous Attributes in C4.5
  • J. R. Quinlan
  • Computer Science
  • J. Artif. Intell. Res.
  • 29 February 1996
TLDR
A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes with an MDL-inspired penalty, leading to smaller decision trees with higher predictive accuracies. Expand
Bagging, Boosting, and C4.5
TLDR
Results of applying Breiman's bagging and Freund and Schapire's boosting to a system that learns decision trees and testing on a representative collection of datasets show boosting shows the greater benefit. Expand
Simplifying Decision Trees
  • J. R. Quinlan
  • Computer Science
  • Int. J. Man Mach. Stud.
  • 1 September 1987
TLDR
Techniques for simplifying decision trees while retaining their accuracy are discussed, described, illustrated, and compared on a test-bed of decision trees from a variety of domains. Expand
Learning Efficient Classification Procedures and Their Application to Chess End Games
A series of experiments dealing with the discovery of efficient classification procedures from large numbers of examples is described, with a case study from the chess end game king-rook versusExpand
Learning logical definitions from relations
TLDR
foil is a system that learns Horn clauses from data expressed as relations, based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. Expand
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