TY - GEN
T1 - A mixed learning strategy for finding typical testors in large datasets
AU - González-Guevara, Víctor Iván
AU - Godoy-Calderon, Salvador
AU - Alba-Cabrera, Eduardo
AU - Ibarra-Fiallo, Julio
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper presents a mixed, global and local, learning strategy for finding typical testors in large datasets. The goal of the proposed strategy is to allow any search algorithm to achieve the most significant reduction possible in the search space of a typical testor-finding problem. The strategy is based on a trivial classifier which partitions the search space into four distinct classes and allows the assessment of each feature subset within it. Each class is handled by slightly different learning actions, and induces a different reduction in the search-space of a problem. Any typical testor-finding algorithm, whether deterministic or metaheuristc, can be adapted to incorporate the proposed strategy and can take advantage of the learned information in diverse manners.
AB - This paper presents a mixed, global and local, learning strategy for finding typical testors in large datasets. The goal of the proposed strategy is to allow any search algorithm to achieve the most significant reduction possible in the search space of a typical testor-finding problem. The strategy is based on a trivial classifier which partitions the search space into four distinct classes and allows the assessment of each feature subset within it. Each class is handled by slightly different learning actions, and induces a different reduction in the search-space of a problem. Any typical testor-finding algorithm, whether deterministic or metaheuristc, can be adapted to incorporate the proposed strategy and can take advantage of the learned information in diverse manners.
KW - Algorithms
KW - Feature selection
KW - Testor theory
UR - https://www.scopus.com/pages/publications/84983523260
U2 - 10.1007/978-3-319-25751-8_86
DO - 10.1007/978-3-319-25751-8_86
M3 - Contribución a la conferencia
AN - SCOPUS:84983523260
SN - 9783319257501
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 716
EP - 723
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Pardo, Alvaro
A2 - Kittler, Josef
PB - Springer Verlag
T2 - 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
Y2 - 9 November 2015 through 12 November 2015
ER -