loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: G. Armano and E. Tamponi

Affiliation: University of Cagliari, Italy

Keyword(s): Data Analysis, Preprocessing, Complexity, Estimation.

Related Ontology Subjects/Areas/Topics: Classification ; Meta Learning ; Pattern Recognition ; Theory and Methods

Abstract: Systems for complexity estimation typically aim to quantify the overall complexity of a domain, with the goal of comparing the hardness of different datasets or to associate a classification task to an algorithm that is deemed best suited for it. In this work we describe MultiResolution Complexity Analysis, a novel method for partitioning a dataset into regions of different classification complexity, with the aim of highlighting sources of complexity or noise inside the dataset. Initial experiments have been carried out on relevant datasets, proving the effectiveness of the proposed method.

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 44.212.93.133

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:
Armano, G. and Tamponi, E. (2015). MultiResolution Complexity Analysis - A Novel Method for Partitioning Datasets into Regions of Different Classification Complexity. In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-076-5; ISSN 2184-4313, SciTePress, pages 334-341. DOI: 10.5220/0005247003340341

@conference{icpram15,
author={G. Armano. and E. Tamponi.},
title={MultiResolution Complexity Analysis - A Novel Method for Partitioning Datasets into Regions of Different Classification Complexity},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2015},
pages={334-341},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005247003340341},
isbn={978-989-758-076-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - MultiResolution Complexity Analysis - A Novel Method for Partitioning Datasets into Regions of Different Classification Complexity
SN - 978-989-758-076-5
IS - 2184-4313
AU - Armano, G.
AU - Tamponi, E.
PY - 2015
SP - 334
EP - 341
DO - 10.5220/0005247003340341
PB - SciTePress