YYC: A Fast Performance Incremental Algorithm for Finding Typical Testors

Alba-Cabrera, Eduardo; Ibarra-Fiallo, Julio; Godoy-Calderón, Salvador; Cervantes-Alonso, Fernando

doi:10.1007/978-3-319-12568-8_51

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…From the incremental test-and-generate strategy group we selected the YYC-algorithm [10], a slight variant of the Berge-multiplication that includes the identification of sets that do not need to be combined (as in the DL-algorithm), a reordering of the analysis of edges (similar as in the BMR-algorithm), and the concept of appropriate set to select the needed combinations (as in the KS-algorithm). From the space-delimited search-with-jumps strategy group we selected the BM-algorithm [17] as described in Section 3.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of these algorithms include the basic Berge-multiplication algorithm [8], Dong and Li's (DL) algorithm [9], and Kavvadias and Stavropoulos' (KS) algorithm [2]. A variant in this same group, that comes from testor theory, is the YYC algorithm [10]. The differences from one algorithm to another lie in the strategies they use to update the set of MHS.…”

Section: A Incremental Test-and-generate Strategymentioning

confidence: 99%

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Symbolic Learning for Improving the Performance of Transversal-Computation Algorithms

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 99%

Section: A Incremental Test-and-generate Strategymentioning

confidence: 99%

Symbolic Learning for Improving the Performance of Transversal-Computation Algorithms

et al. 2019

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“…The exhaustive algorithms take exponential time in the number of features. Some examples of these algorithms include the following: Lex [39], all-NRD [7], Fast-CT-EXT [37], YYC [3], Fast-BR [21], and Parallel-YYC [27]. These algorithms can be easily adapted to return only minimumlength irreducible testors.…”

Section: Related Workmentioning

confidence: 99%

An Algorithm for Computing Minimum-Length Irreducible Testors

et al. 2020

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In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length. INDEX TERMS Feature selection, MDL principle, minimum-length irreducible testors, testor.

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“…Following the opposite view, in the field of Testor Theory it is common knowledge that algorithms for computing irreducible testors follow two main strategies: internal and external. The general strategy for internal algorithms is to iteratively select some entries from the basic matrix and using them to construct irreducible testor candidates [18].…”

Section: A Some Implications Of the Relationmentioning

confidence: 99%

On the Relation Between the Concepts of Irreducible Testor and Minimal Transversal

et al. 2019

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This paper aims at studying the relationship between two rather relevant theoretic fields such as Graph Theory and Testor Theory, deepening in the unexploited relation between the concepts of Minimal Transversal and Irreducible Testor. First, the classic definitions of each concept are provided, and then the relation between them is shown and formalized. Some of the immediate consequences of this relation, in terms of the duality property of transversals and about the equivalence of the result of two specific algorithms, one from each field, are discussed. Finally, we also discuss several future research directions that arise from the relationship between the concepts of Minimal Transversal And Irreducible Testor, and the way in which those directions can potentially benefit the development of theory and algorithms for solving different practical problems in both areas. INDEX TERMS Testor theory, graph theory, irreducible testor, minimal transversal, hitting set.

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YYC: A Fast Performance Incremental Algorithm for Finding Typical Testors

Cited by 12 publications

References 11 publications

Symbolic Learning for Improving the Performance of Transversal-Computation Algorithms

Symbolic Learning for Improving the Performance of Transversal-Computation Algorithms

An Algorithm for Computing Minimum-Length Irreducible Testors

On the Relation Between the Concepts of Irreducible Testor and Minimal Transversal

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