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27
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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rates higher than those of C4.5 and C4.5rules. We then propose a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5rules with respect to error rates, but much more efficient on large samples. RIPPERk obtains error rates lower than or equivalent to C4.5rules
Generating Accurate Rule Sets Without Global Optimization
 IN: PROC. OF THE 15TH INT. CONFERENCE ON MACHINE LEARNING
, 1998
"... The two dominant schemes for rulelearning, C4.5 and RIPPER, both operate in two stages. First they induce an initial rule set and then they refine it using a rather complex optimization stage that discards (C4.5) or adjusts (RIPPER) individual rules to make them work better together. In contrast, t ..."
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Cited by 267 (7 self)
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, this paper shows how good rule sets can be learned one rule at a time, without any need for global optimization. We present an algorithm for inferring rules by repeatedly generating partial decision trees, thus combining the two major paradigms for rule generation  creating rules from decision trees
From Machine Learning Proceedings of the Twelfth International Conference ML Fast Eective Rule Induction
"... wcohenresearchattcom 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 e cient it frequ ..."
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wcohenresearchattcom 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 e cient
FURIA: An Algorithm For Unordered Fuzzy Rule Induction
"... This paper introduces a novel fuzzy rulebased classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the wellknown RIPPER algorithm, a stateoftheart rule learner, while preserving its advantages, such as simple and comprehensible rule sets ..."
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Cited by 17 (0 self)
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This paper introduces a novel fuzzy rulebased classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the wellknown RIPPER algorithm, a stateoftheart rule learner, while preserving its advantages, such as simple and comprehensible rule
Adaptive web sites: Conceptual cluster mining
, 1999
"... The creation of a complex web site is a thorny problem in. user interface design. In IJCAI '97, we challenged the AI community to address this problem by creating adaptive web sites. In response, we investigate the problem of index page synthesis — the automatic creation of pages that facilitat ..."
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Cited by 55 (0 self)
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clustering problem and introduce a novel approach which we call conceptual cluster mining: we search for a small number of cohesive clusters that correspond to concepts in a given concept description language L. Next, we present SGML, an algorithm schema that combines a statistical clustering algorithm
IREP++, a faster rule learning algorithm
 In Proceedings of the Fourth SIAM International Conference on Data Mining
, 2004
"... We present IREP++, a rule learning algorithm similar to RIPPER and IREP. Like these other algorithms IREP++ produces accurate, human readable rules from noisy data sets. However IREP++ is able to produce such rule sets more quickly and can often express the target concept with fewer rules and fewer ..."
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Cited by 9 (1 self)
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We present IREP++, a rule learning algorithm similar to RIPPER and IREP. Like these other algorithms IREP++ produces accurate, human readable rules from noisy data sets. However IREP++ is able to produce such rule sets more quickly and can often express the target concept with fewer rules and fewer
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
Fast Effective Rule Induction Overview Prepared By:
, 2000
"... “Fast Effective Rule Induction ” is a study prepared by William Cohen of AT&T Bell Laboratories that discusses a rulelearning algorithm specifically designed to compete with C4.5rules by offering competitive accuracy performance, while at the same time, running more efficiently. ..."
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“Fast Effective Rule Induction ” is a study prepared by William Cohen of AT&T Bell Laboratories that discusses a rulelearning algorithm specifically designed to compete with C4.5rules by offering competitive accuracy performance, while at the same time, running more efficiently.
ART: A Hybrid Classification Model
, 2004
"... This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for ‘Association Rule Tree’, builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized “Separate and ..."
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Cited by 11 (6 self)
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This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for ‘Association Rule Tree’, builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized “Separate
TRIPPER: rule learning using taxonomies
 In Proceedings of the 10th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD
, 2006
"... In many application domains, there is a need for learning algorithms that generate accurate as well as comprehensible classifiers. In this paper, we present TRIPPER a rule induction algorithm that extends RIPPER, a widely used rulelearning algorithm. TRIPPER exploits background knowledge in the fo ..."
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Cited by 1 (0 self)
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In many application domains, there is a need for learning algorithms that generate accurate as well as comprehensible classifiers. In this paper, we present TRIPPER a rule induction algorithm that extends RIPPER, a widely used rulelearning algorithm. TRIPPER exploits background knowledge
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