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Fast Effective Rule Induction
, 1995
"... Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recentlyproposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error r ..."
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Cited by 1257 (21 self)
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Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recentlyproposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error
Rule Induction with CN2: Some Recent Improvements
, 1991
"... The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, ..."
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Cited by 381 (2 self)
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, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. Keywords: learning, rule
Applications of Machine Learning and Rule Induction
 Communications of the ACM
, 1995
"... An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper, we review the major paradigms for machine learning, including neural networks, instancebased methods, genetic learning, rule induction, and analytic ..."
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Cited by 118 (11 self)
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An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper, we review the major paradigms for machine learning, including neural networks, instancebased methods, genetic learning, rule induction, and analytic
Reduced complexity rule induction
 In Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI91
, 1991
"... We present an architecture for rule induction that emphasizes compact, reducedcomplexity rules. A new heuristic technique for finding a covering rule set of sample data is described. This technique refines a set of production rules by iteratively replacing a component of a rule with its single best ..."
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Cited by 33 (4 self)
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We present an architecture for rule induction that emphasizes compact, reducedcomplexity rules. A new heuristic technique for finding a covering rule set of sample data is described. This technique refines a set of production rules by iteratively replacing a component of a rule with its single
Lightweight Rule Induction
, 2000
"... A lightweight rule induction method is described that generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal numberofunweighted rules. A new example is classified by applying all rules and assigning the example to the class with the most satisfied rules. The induction m ..."
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Cited by 16 (1 self)
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A lightweight rule induction method is described that generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal numberofunweighted rules. A new example is classified by applying all rules and assigning the example to the class with the most satisfied rules. The induction
Automatically evolving rule induction algorithms
 Proc. of the 17th European Conf. on Machine Learning
"... Abstract. Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorithms that have been often changed to produce better ones. Having these basic algorithms and their components in ..."
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Cited by 11 (4 self)
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Abstract. Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorithms that have been often changed to produce better ones. Having these basic algorithms and their components
LinearTime Rule Induction
 In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
"... The recent emergence of data mining as a major application of machine learning has led to increased interest in fast rule induction algorithms. These are able to efficiently process large numbers of examples, under the constraint of still achieving good accuracy. If e is the number of examples, man ..."
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Cited by 13 (4 self)
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The recent emergence of data mining as a major application of machine learning has led to increased interest in fast rule induction algorithms. These are able to efficiently process large numbers of examples, under the constraint of still achieving good accuracy. If e is the number of examples
Backward Chaining Rule Induction
"... Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe BackwardChaining Rule Induction (BCRI) as a semisupervised mechanism for biasing the search for IFTHEN rules that express plausible feature interactions. B ..."
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Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe BackwardChaining Rule Induction (BCRI) as a semisupervised mechanism for biasing the search for IFTHEN rules that express plausible feature interactions
Three discretization methods for rule induction
 International Journal of Intelligent Systems
, 2001
"... We discuss problems associated with induction of decision rules from data with numerical attributes. Reallife data frequently contain numerical attributes. Rule induction from numerical data requires an additional step called discretization. In this step numerical values are converted into interval ..."
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Cited by 12 (1 self)
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We discuss problems associated with induction of decision rules from data with numerical attributes. Reallife data frequently contain numerical attributes. Rule induction from numerical data requires an additional step called discretization. In this step numerical values are converted
Results 1  10
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