The logical combinatorial approach to pattern recognition, an overview through selected works

Martínez-Trinidad, José Fco.; Guzmán-Arenas, Adolfo

doi:10.1016/s0031-3203(00)00027-3

Cited by 63 publications

(23 citation statements)

References 18 publications

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“…However, T is not a typical testor, because it cannot be formed in DM a SCE d , with d = 2. But columns j 1 , j 4 form a covering of DM, and there are elements a [1,4], a [2,1] which form a SCE d , with d = 2. Therefore, the feature set T = {x 1 , x 4 } is a typical testor of TM of patients.…”

Section: Definitionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sánchez-Díaz¹,

Lazo-Cortés²,

Piza-Davila³

2012

IJCIS

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In this paper, we introduce a fast implementation of the CT EXT algorithm for testor property identification, that is based on an accumulative binary tuple. The fast implementation of the CT EXT algorithm (one of the fastest algorithms reported), is designed to generate all the typical testors from a training matrix, requiring a reduced number of operations. Experimental results using this fast implementation and the comparison with other state-of-the-art algorithms that generate typical testors are presented.

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Section: Definitionmentioning

confidence: 99%

“…In Logical Combinatorial Pattern Recognition (Ref. 1,2 ), feature selection is solved by using Testor Theory (Ref. 3 ).…”

Section: Introductionmentioning

confidence: 99%

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

Sánchez-Díaz¹,

Lazo-Cortés²,

Piza-Davila³

2012

IJCIS

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“…Into the framework of the Logical Combinatorial Pattern Recognition [1], feature selection is done using typical testors [1,2,3].…”

Section: Typical ε: Testors and Feature Selectionmentioning

confidence: 99%

“…Let M be a dissimilarity Boolean matrix (0=similar,1=dissimilar), obtained from feature by feature comparisons of all the pairs of objects from TM belonging to different classes. M has n columns (x 1 ,…,x n ) and k rows (f 1 As it was mentioned, these definitions have as implicit similarity function the total coincidence, where two objects are similar if they are similar in all their features.…”

Section: Typical ε: Testors and Feature Selectionmentioning

confidence: 99%

Feature Selection Using Typical ε: Testors, Working on Dynamical Data

Carrasco-Ochoa

Ruiz-Shulcloper

De-la-Vega-Doría

2004

Lecture Notes in Computer Science

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Abstract. Typical ε:testors are useful to do feature selection in supervised classification problems with mixed incomplete data, where similarity function is not the total coincidence, but it is a one threshold function. In this kind of problems, modifications on the training matrix can appear very frequently. Any modification of the training matrix can change the set of all typical ε:testors, so this set must be recomputed after each modification. But, complexity of algorithms for calculating all typical ε:testors of a training matrix is too high. In this paper we analyze how the set of all typical ε:testors changes after modifications. An alternative method to calculate all typical ε:testors of the modified training matrix is exposed. The new method's complexity is analyzed and some experimental results are shown.

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“…He defined a testor as a subset of features that allows differentiating objects from different classes. Testors are quite useful, especially when an object description contains both qualitative and quantitative features, and maybe they are incomplete (mixed incomplete data) [3].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Architecture for Computing Testors

Rojas

Cumplido

Carrasco-Ochoa

et al. 2007

Intelligent Data Engineering and Automated Learning - IDEAL 2007

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Abstract. Irreducible testors (also named typical testors) are a useful tool for feature selection in supervised classification problems with mixed incomplete data. However, the complexity of computing all irreducible testors of a training matrix has an exponential growth with respect to the number of columns in the matrix. For this reason different approaches like heuristic algorithms, parallel and distributed processing, have been developed. In this paper, we present the design and implementation of a custom architecture for BT algorithm, which allows computing testors from a given input matrix. The architectural design is based on a parallel approach that is suitable for high populated input matrixes. The architecture has been designed to deal with parallel processing of all matrix rows, automatic candidate generation, and can be configured for any size of matrix. The architecture is able to evaluate whether a feature subset is a testor of the matrix and to calculate the next candidate to be evaluated, in a single clock cycle. The architecture has been implemented on a Field Programmable Gate Array (FPGA) device. Results show that it provides significant performance improvements over a previously reported hardware implementation. Implementation results are presented and discussed.

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The logical combinatorial approach to pattern recognition, an overview through selected works

Cited by 63 publications

References 18 publications

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

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

Feature Selection Using Typical ε: Testors, Working on Dynamical Data

FPGA-Based Architecture for Computing Testors

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