01883nam 2200349z- 450 991055710430332120231214133702.0(CKB)5400000000041004(oapen)https://directory.doabooks.org/handle/20.500.12854/69084(EXLCZ)99540000000004100420202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEnsemble Algorithms and Their ApplicationsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 electronic resource (182 p.)3-03936-958-X 3-03936-959-8 In recent decades, the development of ensemble learning methodologies has gained a significant attention from the scientific and industrial community, and found their application in various real-word problems. Theoretical and experimental evidence proved that ensemble models provide a considerably better prediction performance than single models. The main aim of this collection is to present the recent advances related to ensemble learning algorithms and investigate the impact of their application in a diversity of real-world problems. All papers possess significant elements of novelty and introduce interesting ensemble-based approaches, which provide readers with a glimpse of the state-of-the-art research in the domain.Information technology industriesbicsscInformation technology industriesPintelas Panagiotis Eedt1296132Livieris Ioannis EedtPintelas Panagiotis EothLivieris Ioannis EothBOOK9910557104303321Ensemble Algorithms and Their Applications3023791UNINA