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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 / Conférence
Date
2017
Conference title
2nd INNS Conference on Big Data
Conference date
2016-10
Conference city
Thessaloniki
Conference country
Greece
Book title
Advances in Big Data : Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
Book author
Angelov, Plamen; Manolopoulos, Yannis; Iliadis, Lazaros; Roy, Asim; Vellasco, Marley
Publisher
Springer International Publishing
Published in
Cham
ISBN
978-3-319-47897-5
Number of pages
348
Pages
50-60
Publication identifier
10.1007/978-3-319-47898-2_6
Metadata
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Author(s)
Hmida, Hmida
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ben Hamida, Sana cc
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 data

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