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| Title: | Extracting fuzzy classifier rules from mixed data |
| Keywords: | fuzzy classifiers KEEL database k-means algorithm алгоритм k-средних mixed data classification классификация смешанных данных база данных KEEL нечеткие классификаторы |
| Issue Date: | 2024 |
| Citation: | Ostapenko, R. Extracting fuzzy classifier rules from mixed data / R. Ostapenko, I. Hodashinsky // Информационные технологии и нанотехнологии (ИТНТ-2024) : сб. тр. по материалам X Междунар. конф. и молодеж. шк. (г. Самара, 20-24 мая 2024 г.): в 6 т. / М-во науки и высш. образования Рос. Федерации, Самар. нац. исслед. ун-т им. С. П. Королева (Самар. ун-т). - Самара : Изд-во Самар. ун-та, 2024. - Т. 3: Искусственный интеллект : под ред. А. В. Никонорова, 2024. - С. 031262. |
| Abstract: | Real world data cannot exist without mixing continuous and nominal data. This paper presents fuzzy classifier modification for mixed data classification. Basic fuzzy classifiers can only be applied to continuous attribute values and cannot handle mixed data. This problem can be solved by counting nominal attribute frequencies in each cluster. The Kmeans algorithm is used for clusterization. The proposed method was tested on data sets from the KEEL database and demonstrated higher classification accuracy than the method that does not consider nominal data. |
| URI: | http://repo.ssau.ru/jspui/handle/123456789/12412 |
| Appears in Collections: | Информационные технологии и нанотехнологии |
| File | Size | Format | |
|---|---|---|---|
| 978-5-7883-2080-9_2024-031262.pdf | 278.83 kB | Adobe PDF | View/Open |
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