Results 1 - 10 of 3,163

Fast Effective Rule Induction

by William W. Cohen , 1995
"... Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed 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 ..."
Abstract - Cited by 1274 (21 self) - Add to MetaCart
Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed 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

by Peter Clark, Robin Boswell , 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, ..."
Abstract - Cited by 385 (2 self) - Add to MetaCart
, 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

Rule Induction

by Peter Flach, et al.
"... ..."
Abstract - Cited by 9 (0 self) - Add to MetaCart
Abstract not found

Applications of Machine Learning and Rule Induction

by Pat Langley, Herbert A. Simon - 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, instance-based methods, genetic learning, rule induction, and analytic ..."
Abstract - Cited by 119 (11 self) - Add to MetaCart
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, instance-based methods, genetic learning, rule induction, and analytic

Reduced complexity rule induction

by Sholom M. Weiss, Nitin Indurkhya - In Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI-91 , 1991
"... We present an architecture for rule induction that emphasizes compact, reduced-complexity 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 ..."
Abstract - Cited by 33 (4 self) - Add to MetaCart
We present an architecture for rule induction that emphasizes compact, reduced-complexity 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

by Sholom M. Weiss, Nitin Indurkhya , 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 ..."
Abstract - Cited by 17 (1 self) - Add to MetaCart
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

by Alex A. Freitas - 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 ..."
Abstract - Cited by 11 (4 self) - Add to MetaCart
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

Backward Chaining Rule Induction

by Douglas H. Fisher, Mary E. Edgerton, Zhihua Chen, Lianhong Tang, Douglas H. Fisher
"... Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe Backward-Chaining Rule Induction (BCRI) as a semi-supervised mechanism for biasing the search for IF-THEN 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 Backward-Chaining Rule Induction (BCRI) as a semi-supervised mechanism for biasing the search for IF-THEN rules that express plausible feature interactions

Three discretization methods for rule induction

by Jerzy W. Grzymala-busse, Jerzy Stefanowski - International Journal of Intelligent Systems , 2001
"... We discuss problems associated with induction of decision rules from data with numerical attributes. Real-life 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 ..."
Abstract - Cited by 14 (1 self) - Add to MetaCart
We discuss problems associated with induction of decision rules from data with numerical attributes. Real-life data frequently contain numerical attributes. Rule induction from numerical data requires an additional step called discretization. In this step numerical values are converted

An Extended Genetic Rule Induction Algorithm

by Juliet Juan Liu, James Tin-yau Kwok , 2000
"... This paper describes an extension of a GAbased, separate-and-conquer propositional rule induction algorithm called SIA [24]. While the original algorithm is computationally attractive and is also able to handle both nominal and continuous attributes efficiently, our algorithm further improves it by ..."
Abstract - Cited by 28 (0 self) - Add to MetaCart
This paper describes an extension of a GAbased, separate-and-conquer propositional rule induction algorithm called SIA [24]. While the original algorithm is computationally attractive and is also able to handle both nominal and continuous attributes efficiently, our algorithm further improves
Results 1 - 10 of 3,163