Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2020.1748509
Determine OWA operator weights using kernel density estimation
Mingwei Lin
Wenshu Xu
Zhanpeng Lin
Riqing Chen
Sažetak
Some subjective methods should divide input values into local
clusters before determining the ordered weighted averaging
(OWA) operator weights based on the data distribution characteristics
of input values. However, the process of clustering input values
is complex. In this paper, a novel probability density based
OWA (PDOWA) operator is put forward based on the data distribution
characteristics of input values. To capture the local cluster
structures of input values, the kernel density estimation (KDE) is
used to estimate the probability density function (PDF), which fits
to the input values. The derived PDF contains the density information
of input values, which reflects the importance of input
values. Therefore, the input values with high probability densities
(PDs) should be assigned with large weights, while the ones with
low PDs should be assigned with small weights. Afterwards, the
desirable properties of the proposed PDOWA operator are investigated.
Finally, the proposed PDOWA operator is applied to handle
the multicriteria decision making problem concerning the evaluation
of smart phones and it is compared with some existing
OWA operators. The comparative analysis shows that the proposed
PDOWA operator is simpler and more efficient than the
existing OWA operators
Ključne riječi
Aggregation operator; ordered weighted averaging; multi-criteria decision making (MCDM)
Hrčak ID:
254458
URI
Datum izdavanja:
9.2.2021.
Posjeta: 718 *