loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: André Rodrigo da Silva ; Leonardo M. Rodrigues and Luciana de Oliveira Rech

Affiliation: Department of Informatics and Statistics (INE), Federal University of Santa Catarina (UFSC) Rua Delfino Conti, s/n, Trindade, Cx.P. 476, CEP: 88040-900, Florianópolis and Brazil

Keyword(s): Machine Learning, Skewed Classes, Imbalanced Datasets, Binary Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

Abstract: Imbalanced classes constitute very complex machine learning classification problems, particularly if there are not many examples for training, in which case most algorithms fail to learn discriminant characteristics, and tend to completely ignore the minority class in favour of the model overall accuracy. Datasets with imbalanced classes are common in several machine learning applications, such as sales forecasting and fraud detection. Current strategies for dealing with imbalanced classes rely on manipulation of the datasets as a means of improving classification performance. Instead of optimizing classification boundaries based on some measure of distance to points, this work directly optimizes the decision surface, essentially turning a classification problem into a regression problem. We demonstrate that our approach is competitive in comparison to other classification algorithms for imbalanced classes, in addition to achieving different properties.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.200.169.91

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Rodrigo da Silva, A.; Rodrigues, L. and Rech, L. (2019). SurfOpt: A New Surface Method for Optimizing the Classification of Imbalanced Dataset. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 724-730. DOI: 10.5220/0007407107240730

@conference{icaart19,
author={André {Rodrigo da Silva}. and Leonardo M. Rodrigues. and Luciana de Oliveira Rech.},
title={SurfOpt: A New Surface Method for Optimizing the Classification of Imbalanced Dataset},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={724-730},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007407107240730},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - SurfOpt: A New Surface Method for Optimizing the Classification of Imbalanced Dataset
SN - 978-989-758-350-6
IS - 2184-433X
AU - Rodrigo da Silva, A.
AU - Rodrigues, L.
AU - Rech, L.
PY - 2019
SP - 724
EP - 730
DO - 10.5220/0007407107240730
PB - SciTePress