Md-Elm: Originally Mislabeled Samples Detection using Op-Elm Model
Abstract
This Paper Proposes a Methodology for Identifying Data Samples that Are Likely to Be Mislabeled in a C-Class Classification Problem (Dataset). the Methodology Relies on an Assumption that the Generalization Error of a Model Learned from the Data Decreases If a Label of Some Mislabeled Sample is Changed to its Correct Class. a General Classification Model Used in the Paper is Op-Elm; It Also Provides a Fast Way to Estimate the Generalization Error by Press Leave-One-Out. It is Tested on Two Toy Datasets, as Well as on Real Life Datasets for One of Which Expert Knowledge About the Identified Potential Mislabels Has Been Sought.
Recommended Citation
A. Akusok et al., "Md-Elm: Originally Mislabeled Samples Detection using Op-Elm Model," Neurocomputing, vol. 159, no. 1, pp. 242 - 250, Elsevier, Jan 2015.
The definitive version is available at https://doi.org/10.1016/j.neucom.2015.01.055
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Classification; Extreme learning machine; Mislabels
International Standard Serial Number (ISSN)
1872-8286; 0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2015
