A Fast Implementation for the Typical Testor Property Identification Based on an Accumulative Binary Tuple

Sánchez-Díaz, Guillermo; Lazo-Cortés, Manuel S.; Piza-Davila, Ivan

doi:10.1080/18756891.2012.747657

Cited by 14 publications

(11 citation statements)

References 13 publications

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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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Section: Related Workmentioning

confidence: 99%

An Algorithm for Computing Minimum-Length Irreducible Testors

et al. 2020

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“…the number of subsets not tested), and the specific procedure applied to a subset for testing if it is a MHS or not, determine the differences among algorithms of this last sub-group. Representative algorithms of this strategy are LEX [15], FastCT [16], and most of all, the Binary-Recursive (BR) algorithm [17]) that orders the edges in the input incidence matrix by increasing cardinality, and then searches the space of vertex subsets following a particular order which is a combination of the cardinality and lexicographic orders. It also includes the same appropriate concept used in the KS algorithm (known as compatible set) and combines hitting sets from different levels of the ordered search tree.…”

Section: B Space-delimited Search-with-jumps Strategymentioning

confidence: 99%

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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“…Three external TTAs were selected for experimentation with representative TMs. These algorithms are BT [11], LEX [13], and FastCTExt [12]. In the next section, all the above mentioned algorithms are tested against specifically selected families of TMs, and the obtained results discussed.…”

Section: Taxonomy and Nature Of Ttasmentioning

confidence: 99%

“…The problem of finding the set Ψ * (A) of all typical testors in a basic matrix A is an old problem that has had an important development in the last ten years. To support this statement, consider the number of published papers with new algorithms related to this problem [3] [13] [7] [12].…”

Section: Introductionmentioning

confidence: 99%

A Theoretical and Practical Framework for Assessing the Computational Behavior of Typical Testor-Finding Algorithms

Alba-Cabrera

Ibarra-Fiallo

Godoy-Calderón

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Although the general relevance of Testor Theory as the theoretical ground for useful feature selection procedures is well known, there are no practical means, nor any standard methodologies, for assessing the behavior of a testor-finding algorithm when faced with specific circumstances. In this work, we present a practical framework, with proven theoretical foundation, for assessing the behavior of both deterministic and meta-heuristic testor-finding algorithms when faced with specific phenomena.

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A Fast Implementation for the Typical Testor Property Identification Based on an Accumulative Binary Tuple

Cited by 14 publications

References 13 publications

An Algorithm for Computing Minimum-Length Irreducible Testors

An Algorithm for Computing Minimum-Length Irreducible Testors

Symbolic Learning for Improving the Performance of Transversal-Computation Algorithms

A Theoretical and Practical Framework for Assessing the Computational Behavior of Typical Testor-Finding Algorithms

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