An analysis about the measure quality of similarity and its applications in machine learning
Yaima Filiberto, Rafael Bello, Yaile Caballero, Mabel Frias
Available Online October 2013.
- https://doi.org/10.2991/.2013.16How to use a DOI?
- quality of similarity measure, granular computing, rough set theory, machine learning
- 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.
- 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 -