MultiResolution Complexity Analysis - A Novel Method for Partitioning Datasets into Regions of Different Classification Complexity

G. Armano, E. Tamponi

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.

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Paper Citation


in Harvard Style

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, pages 334-341. DOI: 10.5220/0005247003340341


in Bibtex Style

@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},
}


in EndNote Style

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
AU - Armano G.
AU - Tamponi E.
PY - 2015
SP - 334
EP - 341
DO - 10.5220/0005247003340341