Proceedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support

An analysis about the measure quality of similarity and its applications in machine learning

Authors
Yaima Filiberto, Rafael Bello, Yaile Caballero, Mabel Frias
Corresponding Author
Yaima Filiberto
Available Online October 2013.
DOI
https://doi.org/10.2991/.2013.16How to use a DOI?
Keywords
quality of similarity measure, granular computing, rough set theory, machine learning
Abstract
In this paper, a review about the quality of the similarity measure and its applications in machine learning is presented. This measure is analyzed from the perspective of the granular computing. The granular computing allows analyzing the information at different levels of abstraction and from different approaches. The analysis shows that this measure is based on two basic aspects on the universe of objects: the granularity of the information and the principle that, similar problems have similar solutions. Using the measure, a method was formulated to build relations of similarity; these relations and other results have been used in improving machine learning techniques.
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Proceedings
Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
978-90-78677-86-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/.2013.16How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yaima Filiberto
AU  - Rafael Bello
AU  - Yaile Caballero
AU  - Mabel Frias
PY  - 2013/10
DA  - 2013/10
TI  - An analysis about the measure quality of similarity and its applications in machine learning
BT  - Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support
PB  - Atlantis Press
SP  - 130
EP  - 139
SN  - 1951-6851
UR  - https://doi.org/10.2991/.2013.16
DO  - https://doi.org/10.2991/.2013.16
ID  - Filiberto2013/10
ER  -