RT info:eu-repo/semantics/article
T1 Instance selection of linear complexity for big data
A1 Arnaiz González, Álvar
A1 Diez Pastor, José Francisco
A1 Rodríguez Diez, Juan José
A1 García Osorio, César
K1 Nearest neighbor
K1 Data reduction
K1 Instance selection
K1 Hashing
K1 Big data
K1 Informática
K1 Computer science
AB Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets.In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing   to find similarities between instances. While the complexity of conventional methods (usually quadratic, O(n2), or log-linear, O(nlogn)) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances).
PB Elsevier
SN 0950-7051
YR 2016
FD 2016-09
LK http://hdl.handle.net/10259/4221
UL http://hdl.handle.net/10259/4221
LA eng
NO Supported by the Research Projects TIN 2011-24046 and TIN 2015-67534-P from the Spanish Ministry of Economy and Competitiveness.
DS Repositorio Institucional de la Universidad de Burgos
RD 24-dic-2024