An n-ary λ-averaging based similarity classifier

Onesfole Kurama; Pasi Luukka; Mikael Collan

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 2, page 407-421
  • ISSN: 1641-876X

Abstract

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We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ-averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier.

How to cite

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Onesfole Kurama, Pasi Luukka, and Mikael Collan. "An n-ary λ-averaging based similarity classifier." International Journal of Applied Mathematics and Computer Science 26.2 (2016): 407-421. <http://eudml.org/doc/280116>.

@article{OnesfoleKurama2016,
abstract = {We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ-averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier.},
author = {Onesfole Kurama, Pasi Luukka, Mikael Collan},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {similarity classifier with λ-averaging; n-ary λ-averaging operator; n-ary t-norm; n-ary t-conorm; classification},
language = {eng},
number = {2},
pages = {407-421},
title = {An n-ary λ-averaging based similarity classifier},
url = {http://eudml.org/doc/280116},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Onesfole Kurama
AU - Pasi Luukka
AU - Mikael Collan
TI - An n-ary λ-averaging based similarity classifier
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 2
SP - 407
EP - 421
AB - We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ-averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier.
LA - eng
KW - similarity classifier with λ-averaging; n-ary λ-averaging operator; n-ary t-norm; n-ary t-conorm; classification
UR - http://eudml.org/doc/280116
ER -

References

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