Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Study
Hmida, Hmida; Ben Hamida, Sana; Borgi, Amel; Rukoz, Marta (2017), Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Study, in Angelov, Plamen; Manolopoulos, Yannis; Iliadis, Lazaros; Roy, Asim; Vellasco, Marley, Advances in Big Data : Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece, Springer International Publishing : Cham, p. 50-60. 10.1007/978-3-319-47898-2_6
Type
Communication / ConférenceDate
2017Conference title
2nd INNS Conference on Big DataConference date
2016-10Conference city
ThessalonikiConference country
GreeceBook title
Advances in Big Data : Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, GreeceBook author
Angelov, Plamen; Manolopoulos, Yannis; Iliadis, Lazaros; Roy, Asim; Vellasco, MarleyPublisher
Springer International Publishing
Published in
Cham
ISBN
978-3-319-47897-5
Number of pages
348Pages
50-60
Publication identifier
Metadata
Show full item recordAuthor(s)
Hmida, HmidaLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ben Hamida, Sana

Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Borgi, Amel
Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
Rukoz, Marta
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
The amount of available data for data mining, knowledge discovery continues to grow very fast with the era of Big Data. Genetic Programming algorithms (GP), that are efficient machine learning techniques, are face up to a new challenge that is to deal with the mass of the provided data. Active Sampling, already used for Active Learning, might be a good solution to improve the Evolutionary Algorithms (EA) training from very big data sets. This paper investigates the adaptation of Topology Based Selection (TBS) to face massive learning datasets by means of Hierarchical Sampling. We propose to combine the Random Subset Selection (RSS) with the TBS to create the RSS-TBS method. Two variants are implemented, applied to solve the KDD intrusion detection problem. They are compared to the original RSS, TBS techniques. The experimental results show that the important computational cost generated by original TBS when applied to large datasets can be lightened with the Hierarchical Sampling.Subjects / Keywords
Sampling; machine learning; decision support systems; Big dataRelated items
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