A fusion method for multi-valued data
In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penal...
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| Published in: | Information fusion Vol. 71; pp. 1 - 10 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01-07-2021
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| Abstract | In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.
•We have expanded the target of deviation-based functions to the multivariable case.•We provide a new construction method for this type of deviation-based functions.•Our approach is not restricted to aggregating values of the unit interval.•Temporal complexity is lower than that of alternatives such as penalty functions.•Our method can be applied in image reduction, deep learning or decision making. |
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| AbstractList | In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.
•We have expanded the target of deviation-based functions to the multivariable case.•We provide a new construction method for this type of deviation-based functions.•Our approach is not restricted to aggregating values of the unit interval.•Temporal complexity is lower than that of alternatives such as penalty functions.•Our method can be applied in image reduction, deep learning or decision making. |
| Author | Rodríguez-Martínez, Iosu Bustince, Humberto Papčo, Martin Altalhi, Abdulrahman H. Fumanal-Idocin, Javier |
| Author_xml | – sequence: 1 givenname: Martin surname: Papčo fullname: Papčo, Martin email: papco@ruzomberok.sk organization: Catholic University in Ružomberok, Hrabovská cesta 1, 034 01 Ružomberok, Slovak Republic – sequence: 2 givenname: Iosu orcidid: 0000-0002-9960-0203 surname: Rodríguez-Martínez fullname: Rodríguez-Martínez, Iosu email: iosu.rodriguez@unavarra.es organization: Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain – sequence: 3 givenname: Javier orcidid: 0000-0002-0644-1355 surname: Fumanal-Idocin fullname: Fumanal-Idocin, Javier organization: Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain – sequence: 4 givenname: Abdulrahman H. surname: Altalhi fullname: Altalhi, Abdulrahman H. organization: Faculty of Computing and Information Technology, King Abdulaziz University, 80203, Jeddah 21589, Saudi Arabia – sequence: 5 givenname: Humberto surname: Bustince fullname: Bustince, Humberto email: bustince@unavarra.es organization: Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain |
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| Cites_doi | 10.1016/j.inffus.2011.10.002 10.1016/j.ins.2010.01.022 10.1016/j.ins.2014.05.024 10.1109/5.726791 10.1016/j.inffus.2019.10.005 10.1016/j.fss.2014.03.008 10.1109/TPAMI.2010.161 10.1109/TIP.2003.819861 10.1016/0165-0114(95)00107-7 10.1016/j.fss.2009.05.012 10.1016/0020-0255(93)90104-T 10.1016/j.patcog.2011.01.017 10.1016/j.fss.2016.09.011 10.1016/j.ins.2017.12.029 10.1016/j.asoc.2017.05.050 10.1007/978-3-030-19494-9_24 10.1016/j.neucom.2020.06.028 10.1016/j.ins.2018.12.090 10.1109/TFUZZ.2003.822679 10.5486/PMD.1972.19.1-4.26 10.1080/03081079708945181 10.2991/eusflat-19.2019.15 10.1016/S0377-2217(02)00538-6 10.1109/TIP.2011.2168412 10.1016/j.fss.2017.03.016 |
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| Keywords | Aggregation fusion Moderate deviation function Multi-valued data fusion |
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| SubjectTerms | Aggregation fusion Moderate deviation function Multi-valued data fusion |
| Title | A fusion method for multi-valued data |
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