Measures of ruleset quality for general rules extraction methods

Holeňa, Martin

doi:10.1016/j.ijar.2009.03.002

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…The extraction of rules from data in a well-known research topic [2,5,6,7,15,19,22,23,24,26,27,33], and it is not covered in this paper. We relay on existing rule-generating tools.…”

Section: International Scientific Publications and Consulting Servicesmentioning

confidence: 99%

Semantic-based Analysis of Rules for Decision Support for Software Maintenance

Reformat¹,

Musílek²,

Kapoor³

et al. 2016

The Oxford Journal of Intelligent Decision and Data Science

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Decision support systems assist users in making suitable and competent decisions regarding variety of issues. They embed knowledge about facts and features related to topics that are subjects of decision-making processes and use this knowledge to identify most appropriate alternatives. Representation of knowledge could take multiple forms. Quite often, if-then rules are used, as they are perceived as one of the easiest ways of capturing knowledge. In general, multiple methods can be employed to construct rulessome of them are fully depended on the experts' knowledge, some on available data, yet some on a combination of both approaches. The paper introduces a methodology for identifying most suitable and representative if-then rules. Semanticbased analysis of these rules is described. All rules are evaluated based on their classification performance, as well as their ability to represent knowledge. This constitutes a step towards an automatic construction of rule-based decision-making systems.

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“…The extraction of rules from data in a well-known research topic [2,5,6,7,15,19,22,23,24,26,27,33], and it is not covered in this paper. We relay on existing rule-generating tools.…”

Section: International Scientific Publications and Consulting Servicesmentioning

confidence: 99%

Semantic-based Analysis of Rules for Decision Support for Software Maintenance

Reformat¹,

Musílek²,

Kapoor³

et al. 2016

The Oxford Journal of Intelligent Decision and Data Science

View full text Add to dashboard Cite

show abstract

“…Further works for the evaluation of the outputs could include the use of aggregation of measures (Barthélemy et al 2006). For this purpose, the selection of an optimal set of measures could be done such as in (Collard and Vansnick 2007), the ranking of the produced rules such as in (Toloo, Sohrabi, and Nalchigar 2009), the use of quality measures of whole sets of rules (Holeňa 2009).…”

Section: Optimal Rule Analysismentioning

confidence: 99%

Optimonotone Measures for Optimal Rule Discovery

Bras¹,

Lenca²,

Lallich³

2012

Computational Intelligence

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Many studies have shown the limits of the support/confidence framework used in Apriori ‐like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support, but candidate set generation (e.g., frequent item set mining) is still costly. In addition, many rules are uninteresting or redundant and one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue. One solution is to not use frequent itemset mining and to focus as soon as possible on interesting rules using additional interestingness measures. We present here a formal framework that allows us to make a link between analytic and algorithmic properties of interestingness measures. We introduce the notion of optimonotony in relation with the optimal rule discovery framework. We then demonstrate a necessary and sufficient condition for the existence of optimonotony. This result can thus be applied to classify the measures. We study the case of 39 classical measures and show that 31 of them are optimonotone. These optimonotone measures can thus be used with an underlying pruning strategy. Empirical evaluations show that the pruning strategy is efficient and leads to the discovery of nuggets using an optimonotone measure and without the support constraint.

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Global Quality Measures for Fuzzy Association Rule Bases

Rusnok

Burda

2017

Advances in Fuzzy Logic and Technology 2017

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Measures of ruleset quality for general rules extraction methods

Cited by 11 publications

References 43 publications

Semantic-based Analysis of Rules for Decision Support for Software Maintenance

Semantic-based Analysis of Rules for Decision Support for Software Maintenance

Optimonotone Measures for Optimal Rule Discovery

Global Quality Measures for Fuzzy Association Rule Bases

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