Similarity metrics vs human judgment of similarity for binary data: Which is best to predict typicality?

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73627221" target="_blank" >RIV/61989592:15310/24:73627221 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1568494624000449" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494624000449</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2024.111270" target="_blank" >10.1016/j.asoc.2024.111270</a>

Result language
angličtina
Original language name
Similarity metrics vs human judgment of similarity for binary data: Which is best to predict typicality?
Original language description
Similarity measures for binary data have been subject to a number of comparative studies. In contrast to these studies, we provide a comparison of similarity measures with human judgment of similarity. For this purpose, we utilize the phenomenon of typicality, whose definition is based on similarity. We observe how well the similarity of objects – either computed by a similarity measure or provided by human judgment – enables the prediction of typicality of these objects in various human categories. In doing so, we examine a large variety of existing similarity measures, and utilize recently available extensive data involving binary data as well as data on human judgment of similarity and typicality.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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