A fast implementation of the CT_EXT algorithm for the testor property identification

Authors:
Guillermo Sanchez-Diaz;Ivan Piza-Davila;Manuel Lazo-Cortes;Miguel Mora-Gonzalez;Javier Salinas-Luna
Affiliations:
Universidad de Guadalajara, Centro Universitario de los Valles, Ameca, Jal., Mexico;Instituto Tecnologico y de Estudios Superiores de Occidente, Tlaquepaque, Jal., Mexico;Universidad de las Ciencias Informaticas, Havana, Cuba;Universidad de Guadalajara, Centro Universitario de los Lagos, Lagos de Moreno, Jal., Mexico;Universidad de Guadalajara, Centro Universitario de los Valles, Ameca, Jal., Mexico
Venue:
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Year:
2010

Citing 4
Cited 1

BR: A New Method for Computing All Typical Testors

CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
CT-EXT: an algorithm for computing typical testor set

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Using typical testors for feature selection in text categorization

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
On the design and implementation of a high performance configurable architecture for testor identification

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications

Typical testors generation based on an evolutionary algorithm

IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Typical testors are a useful tool for both feature selection and for determining feature relevance in supervised classication problems. Nowadays, generating all typical testors of a training matrix is computationally expensive; all reported algorithms have exponential complexity, depending mainly on the number of columns in the training matrix. For this reason, different approaches such as sequential and parallel algorithms, genetic algorithms and hardware implementations techniques have been developed. In this paper, we introduce a fast implementation of the algorithm CT_EXT (which is one of the fastest algorithms reported) based on an accumulative binary tuple, developed for generating all typical testors of a training matrix. The accumulative binary tuple implemented in the CT_EXT algorithm, is a useful way to simplifies the search of feature combinations which fulfill the testor property, because its implementation decreases the number of operations involved in the process of generating all typical testors. In addition, experimental results using the proposed fast implementation of the CT_EXT algorithm and the comparison with other state of the art algorithms that generated typical testors are presented.