2016
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César
Instance selection of linear complexity for big data Journal Article
In: Knowledge-Based Systems, vol. 107, pp. 83–95, 2016, ISSN: 0950-7051.
Abstract | Links | BibTeX | Tags: Big data, Data Mining, Data reduction, Hashing, Instance selection, Nearest neighbors, SELECTED
@article{ArnaizGonzálezLSHIS2016,
title = {Instance selection of linear complexity for big data},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio},
url = {http://www.sciencedirect.com/science/article/pii/S0950705116301617},
doi = {10.1016/j.knosys.2016.05.056},
issn = {0950-7051},
year = {2016},
date = {2016-01-01},
journal = {Knowledge-Based Systems},
volume = {107},
pages = {83–95},
abstract = {Abstract 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 ( n 2 ) , or log-linear, O ( n log n ) ) 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).},
keywords = {Big data, Data Mining, Data reduction, Hashing, Instance selection, Nearest neighbors, SELECTED},
pubstate = {published},
tppubtype = {article}
}
Abstract 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 ( n 2 ) , or log-linear, O ( n log n ) ) 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).