Hierarchical Data Topology Based Selection for Large Scale Learning
Hmida, Hmida; Ben Hamida, Sana; Borgi, Amel; Rukoz, Marta (2016), Hierarchical Data Topology Based Selection for Large Scale Learning, in El Baz, Didier; Bourgeois, Julien, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 1221-1226. 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186
Type
Communication / ConférenceDate
2016Conference title
UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016Conference date
2016-07Conference city
ToulouseConference country
FranceBook title
2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World CongressBook author
El Baz, Didier; Bourgeois, JulienPublisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
ISBN
978-1-5090-2770-5
Number of pages
1242Pages
1221-1226
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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