Rule Induction

@inproceedings{GrzymalaBusse2005RuleI,
  title={Rule Induction},
  author={Jerzy W. Grzymala-Busse},
  booktitle={Data Mining and Knowledge Discovery Handbook},
  year={2005}
}
This chapter begins with a brief discussion of some problems associated with input data. Then different rule types are defined. Three representative rule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed. 
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References

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The paper presents the system LERS for rule induction, which handles inconsistencies in the input data due to its usage of rough set theory principle and induces all rules, each in the minimal form, that can be induced from the inputData. Expand
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A systematic individuals-as-terms approach to knowledge representation for inductive learning based on the use of higher-order logic for knowledge representation is provided and the utility of types and higher- order constructs for this purpose is demonstrated. Expand
Principled Constructive Induction
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It is shown how the proposed framework can be used to combine techniques for selection of representative examples with techniques for construction of new features, in order to solve difficult problems in learning from examples. Expand
The CN2 Induction Algorithm
TLDR
A description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Expand
Using the m -estimate in rule induction
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This work replaces the Laplace estimate in the rule induction system CN2 with a general Bayesian probability estimate, the m- estimate, which does not rely on the Laplacian assumption of equally likely classes and allows for adapting to the learning domain. Expand
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Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exists manyExpand
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The paper describes knowledge acquisition under uncertainty using rough set theory, a concept introduced by Z. Pawlak in 1981, and shows that some classifications are theoretically (and, therefore, in practice) forbidden. Expand
A New Version of the Rule Induction System LERS
TLDR
A new version of the rule induction system LERS is described and compared with the old version and the new LERS system performance is fully comparable with performance of the other two systems. Expand
Fast Eeective Rule Induction
TLDR
This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modiications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more eecient on large samples. Expand
Rule Induction with CN2: Some Recent Improvements
TLDR
Improvements to the CN2 algorithm are described, including the use of the Laplacian error estimate as an alternative evaluation function and it is shown how unordered as well as ordered rules can be generated. Expand
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