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On the Usability of Probably Approximately Correct Implication Bases

P. 72–88.
Borchmann D., Hanika T., Obiedkov S.

We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use cases. To this end, we quantitatively examine the behavior of probably approximately correct implication bases on artificial and real-world data sets and compare their precision and recall with respect to their corresponding exact implication bases. Using a small example, we also provide evidence suggesting that implications from probably approximately correct bases can still represent meaningful knowledge from a given data set.

Language: English
DOI
Text on another site
Keywords: FCA (Formal Concept Analysis)implicationsQuery learningPAC learning
Publication based on the results of:
Explanation-oriented Methods of  Data Analysis for Semantically Rich Data and Their Applications (2017)

In book

Formal Concept Analysis: 14th International Conference, ICFCA 2017, Rennes, France, June 13-16, 2017, Proceedings
Formal Concept Analysis: 14th International Conference, ICFCA 2017, Rennes, France, June 13-16, 2017, Proceedings
Vol. 10308. , Cham: Springer, 2017.
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