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Tuning Active Sampling Techniques for Evolutionary Learner from Big Data Sets: Review and Discussion

Ben Hamida, Sana; Rukoz, Marta (2016), Tuning Active Sampling Techniques for Evolutionary Learner from Big Data Sets: Review and Discussion, dans Didier El Baz, Julien Bourgeois, Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016 Intl IEEE Conferences, IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 1206-1213. 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0184

Type
Communication / Conférence
Date
2016
Titre de l'ouvrage
Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016 Intl IEEE Conferences
Auteurs de l’ouvrage
Didier El Baz, Julien Bourgeois
Éditeur
IEEE - Institute of Electrical and Electronics Engineers
Ville d’édition
Piscataway, NJ
Isbn
978-1-5090-2770-5
Pages
1206-1213
Identifiant publication
Métadonnées
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Auteur(s)
Ben Hamida, Sana cc

Rukoz, Marta
Résumé (EN)
Big data processing is the new challenge for analytical, machine learning techniques. Many efforts are needed to scale both classic, advanced methods to the the mass of the provided data. Evolutionary learning algorithms (EAL) are robust, effective methods in solving a wide variety of complex learning problems. This paper discusses how to tune the active sampling techniques for EAL to deal with very large training data sets. It introduces the key decisions needed to design an effective active sampling strategy, review the main techniques used with evolutionary algorithms. Then, we discuss how they could be adapted to learn from big training data sets, present some research directions in this domain.
Mots-clés
Training; Training data; Evolutionary computation; Supervised learning; Data mining; Sociology; Statistics