Similarity metrics vs human judgment of similarity for binary data: Which is best to predict typicality?
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...
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| Published in | Applied soft computing Vol. 153; p. 111270 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1568-4946 1872-9681 |
| DOI | 10.1016/j.asoc.2024.111270 |
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| Abstract | 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.
•Comparison of 69 existing similarity measures for binary data is provided.•Human judgment of similarity is also included in the comparison.•Ability of similarities to predict typicality is assessed.•Best similarity measures are identified for prediction of typicality.•Extensive psychological data is utilized enabling the study. |
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| AbstractList | 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.
•Comparison of 69 existing similarity measures for binary data is provided.•Human judgment of similarity is also included in the comparison.•Ability of similarities to predict typicality is assessed.•Best similarity measures are identified for prediction of typicality.•Extensive psychological data is utilized enabling the study. |
| ArticleNumber | 111270 |
| Author | Belohlavek, Radim Mikula, Tomas |
| Author_xml | – sequence: 1 givenname: Radim orcidid: 0000-0003-4924-3233 surname: Belohlavek fullname: Belohlavek, Radim email: radim.belohlavek@acm.org – sequence: 2 givenname: Tomas orcidid: 0000-0002-7514-8523 surname: Mikula fullname: Mikula, Tomas email: mail@tomasmikula.cz |
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| Cites_doi | 10.1016/j.ijar.2021.12.001 10.1111/j.1469-185X.1982.tb00376.x 10.1126/science.140.3566.453 10.3758/BRM.40.4.1030 10.2307/1943124 10.2307/1932444 10.1371/journal.pone.0247751 10.1016/0010-0285(75)90024-9 10.1007/BF02289588 10.1145/321062.321074 10.1016/0010-0285(76)90013-X 10.1021/ci300261r 10.4324/9781032633275-4 10.1007/BF01896809 10.3758/BF03195597 10.1186/s12859-016-1392-z 10.2307/2257810 |
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| Title | Similarity metrics vs human judgment of similarity for binary data: Which is best to predict typicality? |
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