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oai:riubu.ubu.es:10259/42212021-11-10T09:38:16Zcom_10259_5377com_10259_5086com_10259_2604com_10259_4219col_10259_5378col_10259_4220

00925njm 22002777a 4500 dc Arnaiz González, Álvar author Diez Pastor, José Francisco author Rodríguez Diez, Juan José author García Osorio, César author 2016-09 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). 0950-7051 http://hdl.handle.net/10259/4221 10.1016/j.knosys.2016.05.056 Nearest neighbor Data reduction Instance selection Hashing Big data Instance selection of linear complexity for big data