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dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.description.abstract© 2019 IEEE. In real-world applications, sometimes there are uncertainties in the data set whether from the collection process or from the natural language. There are not many algorithms that can deal with this kind of data set. Therefore, in this paper, we develop a linguistic self-organizing feature map (LSOFM) that works with vectors of fuzzy numbers. The algorithm is an extension of the regular self-organizing feature map (SOFM). We found that the results from the LSOFM are similar to that from the SOFM. The results from the LSOFM can provide information that contains all the uncertainties from the input while the SOFM cannot.en_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.subjectPhysics and Astronomyen_US
dc.titleA novel self organizing feature map for uncertain dataen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings - 2019 7th International Conference on Green and Human Information Technology, ICGHIT 2019en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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