Neighborhood Random Classification
| Autor: | Fabien Rico, Diala Ezzeddine, Djamel A. Zighed |
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| Přispěvatelé: | DECCO, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Lumière - Lyon 2 (UL2), FODA, Institut des Sciences de l'Homme (ISH), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-Equipe de Recherche en Ingénierie des Connaissances (ERIC) |
| Rok vydání: | 2013 |
| Předmět: |
Ensemble methods
business.industry Machine learning computer.software_genre Ensemble learning k-nearest neighbors algorithm Random forest relative neighborhood Graphs Support vector machine Combinatorics [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Computer Science::Multimedia Partition (number theory) neighborhood graphs Experimental work Artificial intelligence k-Nearest Neighbors business Gabriel Graphs computer Classifier (UML) Mathematics |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783642400193 GSI Advances in Knowledge Discovery and Data Mining ISBN: 9783642302169 PAKDD (1) Proceeding of The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), May 2012, Kuala Lumpur, Malaysia. pp.98-108, ⟨10.1007/978-3-642-30217-6_9⟩ |
| Popis: | Neighborhood Random Classification Diala Ezzeddine Universit´e de Lyon (Lumi`ere Lyon 2) – Laboratoire ERIC – diala.ezzeddine@univ-lyon2.fr 29 August 2013 D. Ezzeddine (ERIC) Neighborhood Random Classification 29 August 2013 1 / 18 Introduction We propose to use neighborhood graphs in Ensemble Method EM Main purpose : Using neighborhood graphs is a strong alternative In this work, we : Used an EM classifier based on neighborhood, Random Neighborhood Classifier (RNC) Compared RNC to kNN, then to other methods D. Ezzeddine (ERIC) Neighborhood Random Classification 29 August 2013 2 / 18 Introduction Outline 1 Introduction 2 Basic Concepts Classifier Neighborhood Classifiers Partition by Neighborhood Graphs 3 Ensemble Methods 4 Neighborhood Random Classifier 5 Result 6 Conclusion D. Ezzeddine (ERIC) Neighborhood Random Classification 29 August 2013 3 / 18 Introduction Outline 1 Introduction 2 Basic Concepts Classifier Neighborhood Classifiers Partition by Neighborhood Graphs 3 Ensemble Methods 4 Neighborhood Random Classifier 5 Result 6 Conclusion D. Ezzeddine (ERIC) Neighborhood Random Classification 29 August 2013 3 / 18 Introduction Outline 1 Introduction 2 Basic C |
| Databáze: | OpenAIRE |
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