%0 Conference Proceedings
%T Effective Streaming Evolutionary Feature Selection Using Dynamic Optimization
%+ Computer Science Department
%+ Modélisation et d'Implémentation des Systèmes Complexes [Constantine] (MISC)
%A Boulesnane, Abdennour
%A Meshoul, Souham
%Z Part 4: Optimization
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA)
%C Oran, Algeria
%Y Abdelmalek Amine
%Y Malek Mouhoub
%Y Otmane Ait Mohamed
%Y Bachir Djebbar
%I Springer International Publishing
%3 Computational Intelligence and Its Applications
%V AICT-522
%P 329-340
%8 2018-05-08
%D 2018
%R 10.1007/978-3-319-89743-1_29
%K Dynamic optimization problems
%K OSFS
%K Feature selection
%K Streaming features
%K Classification
%Z Computer Science [cs]Conference papers
%X Feature selection is a key issue in machine learning and data mining. A great deal of effort has been devoted to static feature selection. However, with the assumption that features occur over time, methods developed so far are difficult to use if not applicable. Therefore, there is a need to design new methods to deal with streaming feature selection (SFS). In this paper, we propose the use of dynamic optimization to handle the dynamic nature of SFS with the ultimate goal to improve the quality of the evolving subset of selected features. A hybrid model is developed to fish out relevant features set as unnecessary by an online feature selection process. Experimental results show the effectiveness of the proposed framework compared to some state of the art methods.
%G English
%Z TC 5
%2 https://inria.hal.science/hal-01913889/document
%2 https://inria.hal.science/hal-01913889/file/467079_1_En_29_Chapter.pdf
%L hal-01913889
%U https://inria.hal.science/hal-01913889
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-CIIA
%~ IFIP-AICT-522