Fitting aggregation functions to data: Part II - idempotization
Contribuinte(s) |
Carvalho, Joao Paulo Lesot, Marie-Jeanne Kaymak, Uzay Vieira, Susana Bouchon-Meunier, Bernadette Yager, Ronald R. |
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Data(s) |
01/01/2016
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Resumo |
The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the second part of this two-part contribution we deal with a quite common situation in which we have inputs coming from different sources, describing a similar phenomenon, but which have not been properly normalized. In such a case, idempotent and nondecreasing functions cannot be used to aggregate them unless proper preprocessing is performed. The proposed idempotization method, based on the notion of B-splines, allows for an automatic calibration of independent variables. The introduced technique is applied in an R source code plagiarism detection system. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30085791/beliakov-fittingaggregation-evid-2016.pdf |
Direitos |
2016, Springer |
Palavras-Chave | #aggregation functions #weighted quasi-arithmetic means #least squares fitting #idempotence |
Tipo |
Book Chapter |