An Investigation of Noise-Tolerant Relational Concept Learning Algorithms

Brunk, Clifford; Pazzani, Michael J.

doi:10.1016/b978-1-55860-200-7.50080-5

Cited by 52 publications

(36 citation statements)

References 8 publications

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“…Examples of such systems include: (i) Reduced Error Pruning (REP) (Brunk et al, 1991), which incorporates an adaptation of decision tree pruning; (ii) Incremental Reduced Error Pruning (IREP) (Fürnkranz et al, 1994), an enhancement over REP, (iii) Repeated Incremental Pruning to Produce Error Reduction (RIPPER) (Cohen, 1995), a further enhancement over IREP, and (iv) Swap-1 (Weiss et al, 1993). All these systems use the covering algorithm for rule learning, shown in Figure 1, whereby rules are "learned" sequentially based on training examples.…”

Section: Previous Workmentioning

confidence: 99%

Rule Learning with Negation: Issues Regarding Effectiveness

Chua

Coenen

Malcolm

2010

Intelligent Information Processing V

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Abstract. An investigation of rule learning processes that allow the inclusion of negated features is described. The objective is to establish whether the use of negation in inductive rule learning systems is effective with respect to classification. This paper seeks to answer this question by considering two issues relevant to such systems; feature identification and rule refinement. Both synthetic and real datasets are used to illustrate solutions to the identified issues and to demonstrate that the use of negative features in inductive rule learning systems is indeed beneficial.

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Section: Previous Workmentioning

confidence: 99%

Rule Learning with Negation: Issues Regarding Effectiveness

Chua

Coenen

Malcolm

2010

Intelligent Information Processing V

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“…Examples of post pruning algorithms include REP (Reduced Error Pruning algorithm) [2], GROW [5], SSRR [20] and hybrid and incremental post pruning techniques [24].…”

Section: Rule Pruningmentioning

confidence: 99%

Preceding Rule Induction with Instance Reduction Methods

Othman

Bryant

2013

Machine Learning and Data Mining in Pattern Recognition

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Abstract.A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy.

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“…The first stage is a greedy process which constructs an initial rule set. This stage is based on an earlier rule-learning algorithm called incremental reduced error pruning (IREP) [Fürnkranz and Widmer 1994], which in turn is based on earlier work due to Quinlan [1990], Cohen [1993], Brunk and Pazzani [1991], and Pagallo and Haussler [1990]. The second stage is an "optimization" phase which attempts to further improve the compactness and accuracy of the rule set.…”

Section: Rippermentioning

confidence: 99%

Context-sensitive learning methods for text categorization

Cohen

Singer

1996

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR

198

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Two recently implemented machine-learning algorithms, RIPPER and sleeping-experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the "context" of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.

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An Investigation of Noise-Tolerant Relational Concept Learning Algorithms

Abstract: We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach expximentally.

Cited by 52 publications

References 8 publications

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness

Preceding Rule Induction with Instance Reduction Methods

Context-sensitive learning methods for text categorization

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