On the use of Heronian means in a similarity classifier

This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular wit...

Full description

Saved in:
Bibliographic Details
Published in:Iranian journal of fuzzy systems (Online) 2020-09, Vol.17 (5), p.137
Main Author: Kurama, O
Format: Article
Language:English
Subjects:
Classification
Data analysis
Datasets
Fuzzy sets
Pattern recognition systems
Set theory
Similarity measures
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifiers here are tested on four real world data sets, i.e., echocardiogram, fertility, horse-colic, and lung cancer. Three previously studied similarity classifiers are used as benchmarks to the new approaches. We observe that the similarity classifier with a generalized Heronian mean produces good classification results for the tested data sets, and is therefore more suitable for use in these classification problems.
ISSN:1735-0654
2676-4334
DOI:10.22111/ijfs.2020.5521