Bounding Edit Distance for similarity-based sequence classification on Structural Pattern Recognition
Empreu sempre aquest identificador per citar o enllaçar aquest ítem
http://hdl.handle.net/10045/109739
Títol: | Bounding Edit Distance for similarity-based sequence classification on Structural Pattern Recognition |
---|---|
Autors: | Rico-Juan, Juan Ramón | Valero-Mas, Jose J. | Iñesta, José M. |
Grups d'investigació o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Paraules clau: | Structural pattern recognition | Efficient search | Classification | Nearest neighbor | Edit distance |
Àrees de coneixement: | Lenguajes y Sistemas Informáticos |
Data de publicació: | de desembre-2020 |
Editor: | Elsevier |
Citació bibliogràfica: | Applied Soft Computing. 2020, 97(Part A): 106778. https://doi.org/10.1016/j.asoc.2020.106778 |
Resum: | Pattern Recognition tasks in the structural domain generally exhibit high accuracy results, but their time efficiency is quite low. Furthermore, this low performance is more pronounced when dealing with instance-based classifiers, since, for each query, the entire corpus must be evaluated to find the closest prototype. In this work we address this efficiency issue for the Nearest Neighbor classifier when data are encoded as two-dimensional code sequences, and more precisely strings and sequences of vectors. For this, a set of bounds is proposed in the distance metric that avoid the calculation of unnecessary distances. Results obtained prove the effectiveness of the proposal as it reduces the classification time in percentages between 80% and 90% for string representations and between 60% and 80% for data codified as sequences of vectors with respect to their corresponding non-optimized version of the classifier. |
Patrocinadors: | This research work was partially funded by “Programa I+D+i de la Generalitat Valenciana, Spain” through grant APOSTD/2020/256 by the Spanish Ministerio de Economía, Industria y Competitividad through Project HISPAMUS (No. TIN2017-86576-R supported by EU FEDER funds). |
URI: | http://hdl.handle.net/10045/109739 |
ISSN: | 1568-4946 (Print) | 1872-9681 (Online) |
DOI: | 10.1016/j.asoc.2020.106778 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2020 Elsevier B.V. |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.1016/j.asoc.2020.106778 |
Apareix a la col·lecció: | INV - GRFIA - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
---|---|---|---|---|
Rico-Juan_etal_2020_ApplSoftCompJ_final.pdf | Versión final (acceso restringido) | 644,89 kB | Adobe PDF | Obrir Sol·licitar una còpia |
Rico-Juan_etal_2020_ApplSoftCompJ_accepted.pdf | Accepted Manuscript (acceso abierto) | 803,65 kB | Adobe PDF | Obrir Vista prèvia |
Tots els documents dipositats a RUA estan protegits per drets d'autors. Alguns drets reservats.