Comparison of two topological approaches for dealing with noisy labeling

Autor:	Stéphane Lallich, Djamel A. Zighed, Fabien Rico, Fabrice Muhlenbach
Rok vydání:	2015
Předmět:	Receiver operating characteristic business.industry Cognitive Neuroscience medicine.medical_treatment Pattern recognition Topology Graph Computer Science Applications Artificial Intelligence medicine Artificial intelligence business Relaxation technique Mathematics
Zdroj:	Neurocomputing. 160:3-17
ISSN:	0925-2312
Popis:	This paper focuses on the detection of likely mislabeled instances in a learning dataset. In order to detect potentially mislabeled samples, two solutions are considered which are both based on the same framework of topological graphs. The first is a statistical approach based on Cut Edges Weighted statistics (CEW) in the neighborhood graph. The second solution is a Relaxation Technique (RT) that optimizes a local criterion in the neighborhood graph. The evaluations by ROC curves show good results since almost 90% of the mislabeled instances are retrieved for a cost of less than 20% of false positive. The removal of samples detected as mislabeled by our approaches generally leads to an improvement of the performances of classical machine learning algorithms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f5d5bdb3f5abf56c8cf414eb0f813546 https://doi.org/10.1016/j.neucom.2014.10.087 Zobrazit plný text záznamu Full Text from ScienceDirect