%0 Conference Proceedings
%T Neighborhood Random Classification
%+ DECCO
%+ FODA
%+ FODA
%A Rico, Fabien
%A Zighed, Djamel Abdelkader
%A Ezzeddine, Diala
%< avec comité de lecture
%Z ERIC:11-036
%( Proceeding of The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
%B The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
%C Kuala Lumpur, Malaysia
%3 (accepté)
%P 98-108
%8 2012-05-29
%D 2012
%R 10.1007/978-3-642-30217-6_9
%K Ensemble methods
%K neighborhood graphs
%K relative neighborhood Graphs
%K Gabriel Graphs
%K k-Nearest Neighbors
%Z Computer Science [cs]/Machine Learning [cs.LG]Conference papers
%X Ensemble methods (EMs) have become increasingly popular in data mining because of their efficiency. These methods(EMs) generate a set of classifiers using one or several machine learning algorithms (MLAs) and aggregate them into a single classifier (Meta-Classifier, MC). Amon MLAs, k-Nearest Neighbors (kNN) is one of the most known used in the context of EMs. However, handling the parameter k might be difficult. This drawback exists almost for all MLA that are instances based. Here, we propose an approach based on neighborhood graphs as alternative. Thanks to theses related graphs, like relative neighborhood graphs (RNGs) or Gabriel graphs (GGs), we provide a generalized approach with less arbitrary parameters. Introducing neighborhood graphs in EMs approaches has never been done before. The results of our algorithm : Neighborhood Random Classification are very promising since they are equal to the best EMs approaches such as Random Forest or those based on SVMs. In this exploratory and experimental work, we provide the methodological approach and we provide many comparison results.
%G English
%L hal-00660745
%U https://hal.science/hal-00660745
%~ UNIV-ST-ETIENNE
%~ ENS-LYON
%~ UNIV-LYON3
%~ CNRS
%~ UNIV-LYON2
%~ ERIC
%~ UDL
%~ UNIV-LYON