loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Joel Luís Carbonera

Affiliation: Institute of informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Keyword(s): Instance Selection, Data Reduction, Data Mining, Machine Learning, Big Data.

Abstract: Due to the increasing size of the datasets, instance selection techniques have been applied for reducing the computational resources involved in data mining and machine learning tasks. In this paper, we propose a global density-based approach for selecting instances. The algorithm selects only the densest instances in a given neighborhood and the instances in the boundaries among classes, while excludes potentially harmful instances. Our method was evaluated on 14 well-known datasets used in a classification task. The performance of the proposed algorithm was compared to the performances of 8 prototype selection algorithms in terms of accuracy and reduction rate. The experimental results show that, in general, the proposed algorithm provides a good trade-off between reduction rate and accuracy with reasonable time complexity.

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 3.144.89.152

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:
Carbonera, J. (2021). A Global Density-based Approach for Instance Selection. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 402-409. DOI: 10.5220/0010402104020409

@conference{iceis21,
author={Joel Luís Carbonera.},
title={A Global Density-based Approach for Instance Selection},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={402-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010402104020409},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Global Density-based Approach for Instance Selection
SN - 978-989-758-509-8
IS - 2184-4992
AU - Carbonera, J.
PY - 2021
SP - 402
EP - 409
DO - 10.5220/0010402104020409
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic