Trends of Evolutionary Machine Learning to Address Big Data Mining
Ben Hamida, Sana; Benjelloun, Ghita; Hmida, Hmida (2021), Trends of Evolutionary Machine Learning to Address Big Data Mining, in Inès Saad; Camille Rosenthal-Sabroux; Faiez Gargouri; Pierre-Emmanuel Arduin, Information and Knowledge Systems. Digital Technologies, Artificial Intelligence and Decision Making, Springer International Publishing : Berlin Heidelberg, p. 85-99. 10.1007/978-3-030-85977-0_7
Type
Communication / ConférenceExternal document link
https://hal.science/hal-03363083v1Date
2021Conference title
5th International Conference, ICIKS 2021Conference date
2021-06Conference city
Virtual eventConference country
FranceBook title
Information and Knowledge Systems. Digital Technologies, Artificial Intelligence and Decision MakingBook author
Inès Saad; Camille Rosenthal-Sabroux; Faiez Gargouri; Pierre-Emmanuel ArduinPublisher
Springer International Publishing
Published in
Berlin Heidelberg
ISBN
978-3-030-85976-3
Number of pages
185Pages
85-99
Publication identifier
Metadata
Show full item recordAuthor(s)
Ben Hamida, SanaLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Benjelloun, Ghita
PSL Research University, UMR3306
Hmida, Hmida
Université de Tunis El Manar [UTM]
Abstract (EN)
Improving decisions by better mining the available data in an Information System is a common goal in many decision making environments. However, the complexity and the large size of the collected data in modern systems make this goal a challenge for mining methods. Evolutionary Data Mining Algorithms (EDMA), such as Genetic Programming (GP), are powerful meta-heuristics with an empirically proven efficiency on complex machine learning problems. They are expected to be applied to real-world big data tasks and applications in our daily life. Thus, they need, as all machine learning techniques, to be scaled to Big Data bases. This paper review some solutions that could be applied to help EDMA to deal with Big Data challenges. Two solutions are then selected and explained. The first one is based on the algorithmic manipulation involving the introduction of the active learning paradigm thanks to the active data sampling. The second is based on the processing manipulation involving horizontal scaling thanks to the processing distribution over networked nodes. This work explains how each solution is introduced to GP. As preliminary experiences, the extended GP is applied to solve two complex machine learning problem: the Higgs Boson classification problem and the Pulsar detection problem. Experimental results are then discussed and compared to value the efficiency of each solution.Subjects / Keywords
Big data mining; Genetic Programming; Data sampling; Apache Spark; Horizontal parallelization; Active LearningRelated items
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