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Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets
In this paper authors propose a new algorithm for linguistic data summarization based on hybridization of rough sets and fuzzy sets techniques. The new algorithm applies rough sets theory for feature selection in early stages of linguistic summaries’ generation. The rough sets theory was used to red...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338181/ http://dx.doi.org/10.1007/978-3-030-52705-1_29 |
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author | Pérez Pupo, Iliana Piñero Pérez, Pedro Y. Bello, Rafael Acuña, Luis Alvarado García Vacacela, Roberto |
author_facet | Pérez Pupo, Iliana Piñero Pérez, Pedro Y. Bello, Rafael Acuña, Luis Alvarado García Vacacela, Roberto |
author_sort | Pérez Pupo, Iliana |
collection | PubMed |
description | In this paper authors propose a new algorithm for linguistic data summarization based on hybridization of rough sets and fuzzy sets techniques. The new algorithm applies rough sets theory for feature selection in early stages of linguistic summaries’ generation. The rough sets theory was used to reduce on significant way, the amount on summaries obtained by others algorithms. The algorithm combines lower approximation, k grade dependency and fuzzy sets to get linguistic summaries. The results of proposed algorithm are compared with association rules approach. In order to validate the algorithm proposed, authors apply both qualitative and quantitative methods. Authors used two databases in order to validate the algorithm; theses databases belong to “Repository of Project Management Research”. The first database is associated to personality traits and human performance in software projects. The second database is associated to analysis of revenue assurance in different organization. Considering quantitative approach, the algorithm proposed, obtains better results than the algorithm based on association rules; while regards execution time, the best algorithm was the algorithm based on association rules, because rough sets theory was high time-consuming technique. |
format | Online Article Text |
id | pubmed-7338181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73381812020-07-07 Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets Pérez Pupo, Iliana Piñero Pérez, Pedro Y. Bello, Rafael Acuña, Luis Alvarado García Vacacela, Roberto Rough Sets Article In this paper authors propose a new algorithm for linguistic data summarization based on hybridization of rough sets and fuzzy sets techniques. The new algorithm applies rough sets theory for feature selection in early stages of linguistic summaries’ generation. The rough sets theory was used to reduce on significant way, the amount on summaries obtained by others algorithms. The algorithm combines lower approximation, k grade dependency and fuzzy sets to get linguistic summaries. The results of proposed algorithm are compared with association rules approach. In order to validate the algorithm proposed, authors apply both qualitative and quantitative methods. Authors used two databases in order to validate the algorithm; theses databases belong to “Repository of Project Management Research”. The first database is associated to personality traits and human performance in software projects. The second database is associated to analysis of revenue assurance in different organization. Considering quantitative approach, the algorithm proposed, obtains better results than the algorithm based on association rules; while regards execution time, the best algorithm was the algorithm based on association rules, because rough sets theory was high time-consuming technique. 2020-06-10 /pmc/articles/PMC7338181/ http://dx.doi.org/10.1007/978-3-030-52705-1_29 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Pérez Pupo, Iliana Piñero Pérez, Pedro Y. Bello, Rafael Acuña, Luis Alvarado García Vacacela, Roberto Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets |
title | Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets |
title_full | Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets |
title_fullStr | Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets |
title_full_unstemmed | Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets |
title_short | Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets |
title_sort | linguistic summaries generation with hybridization method based on rough and fuzzy sets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338181/ http://dx.doi.org/10.1007/978-3-030-52705-1_29 |
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