%0 Conference Proceedings
%T Stacking Strong Ensembles of Classifiers
%+ University of Patras
%A Alexandropoulos, Stamatios-Aggelos, N.
%A Aridas, Christos
%A Kotsiantis, Sotiris
%A Vrahatis, Michael, N.
%Z Part 10: Machine Learning - Natural Language
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y John MacIntyre
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-559
%P 545-556
%8 2019-05-24
%D 2019
%R 10.1007/978-3-030-19823-7_46
%K Decision support systems
%K Supervised machine learning
%K Ensembles of classifiers
%Z Computer Science [cs]Conference papers
%X A variety of methods have been developed in order to tackle a classification problem in the field of decision support systems. A hybrid prediction scheme which combines several classifiers, rather than selecting a single robust method, is a good alternative solution. In order to address this issue, we have provided an ensemble of classifiers to create a hybrid decision support system. This method based on stacking variant methodology that combines strong ensembles to make predictions. The presented hybrid method has been compared with other known-ensembles. The experiments conducted on several standard benchmark datasets showed that the proposed scheme gives promising results in terms of accuracy in most of the cases.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-02331304/document
%2 https://inria.hal.science/hal-02331304/file/483292_1_En_46_Chapter.pdf
%L hal-02331304
%U https://inria.hal.science/hal-02331304
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-559
%~ TEST3-HALCNRS