A novel self organizing feature map for uncertain data
© 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 featu...
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2020
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th-cmuir.6653943832-677552020-04-02T15:19:31Z A novel self organizing feature map for uncertain data Sansanee Auephanwiriyakul Nipon Theera-Umpon Computer Science Decision Sciences Energy Physics and Astronomy © 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. 2020-04-02T15:02:51Z 2020-04-02T15:02:51Z 2019-01-01 Conference Proceeding 2-s2.0-85074301638 10.1109/ICGHIT.2019.00019 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074301638&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/67755 |
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Chiang Mai University |
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Thailand |
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| topic |
Computer Science Decision Sciences Energy Physics and Astronomy |
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Computer Science Decision Sciences Energy Physics and Astronomy Sansanee Auephanwiriyakul Nipon Theera-Umpon A novel self organizing feature map for uncertain data |
| description |
© 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. |
| format |
Conference Proceeding |
| author |
Sansanee Auephanwiriyakul Nipon Theera-Umpon |
| author_facet |
Sansanee Auephanwiriyakul Nipon Theera-Umpon |
| author_sort |
Sansanee Auephanwiriyakul |
| title |
A novel self organizing feature map for uncertain data |
| title_short |
A novel self organizing feature map for uncertain data |
| title_full |
A novel self organizing feature map for uncertain data |
| title_fullStr |
A novel self organizing feature map for uncertain data |
| title_full_unstemmed |
A novel self organizing feature map for uncertain data |
| title_sort |
novel self organizing feature map for uncertain data |
| publishDate |
2020 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074301638&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/67755 |
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1681426693809504256 |