A Mixed Learning Strategy for Finding Typical Testors in Large Datasets

González-Guevara, Víctor Iván; Godoy-Calderón, Salvador; Alba-Cabrera, Eduardo; Ibarra-Fiallo, Julio

doi:10.1007/978-3-319-25751-8_86

Cited by 5 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The synthetic accumulation of acquired knowledge and its combination with selected background knowledge allows the definition of a learning strategy for optimizing the search. Such a strategy can be adapted to work alongside many minimal hitting set-computation algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Symbolic Learning for Improving the Performance of Transversal-Computation Algorithms

et al. 2019

Self Cite

View full text Add to dashboard Cite

Using the hypergraphs as the central data structure in dynamic discrete association problems is a common practice. The computation of minimal transversals (i.e., the family of all minimal hitting sets) in those hypergraphs is a well-studied task associated with a large number of practical applications. However, both the dynamic nature of the problems and the non-polynomial behavior of all currently known algorithms for that task justify the search for performance optimizations that allow transversal-computation algorithms to consistently handle the potentially large problems while optimizing the use of computational resources. This scenario has been extensively studied from the perspective of the hypergraph, rough set, and testor theories, but this paper presents the first glimpse into a symbolic learning approach. We present a symbolic learning strategy, for the class of transversal-computation algorithms, designed to guide and optimize the search process. Since the proposed strategy is based on the background knowledge about the search space and not on a specific search technique, it can be adapted to a wide variety of algorithms. We present the learning strategy as well as its adaptation into two representative transversal-computation algorithms. The comparative experimental results reveal its computational behavior on different problem families.

show abstract

Section: Introductionmentioning

confidence: 99%