Finding Originally Mislabels with Md-Elm
Abstract
This Paper Presents a Methodology Which Aims at Detecting Mislabeled Samples, with a Practical Example in the Field of Bankruptcy Prediction. Mislabeled Samples Are Found in Many Classification Problems and Can Bias the Training of the Desired Classifier. This Paper Proposes a New Method based on Extreme Learning Machine (Elm) Which Allows for Identification of the Most Probable Mislabeled Samples. Two Datasets Are Used in Order to Validate and Test the Proposed Methodology: A Toy Example (Xor Problem) and a Real Dataset from Corporate Finance (Bankruptcy Prediction).
Recommended Citation
A. Akusok et al., "Finding Originally Mislabels with Md-Elm," 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings, pp. 689 - 694, European Symposium on Artificial Neural Networks, Jan 2014.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-287419095-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Jan 2014
