Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification
The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with stan...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1968.pdf |
| _version_ | 1797234210593832960 |
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| author | Kurnia Muludi Revita Setianingsih Ridho Sholehurrohman Akmal Junaidi |
| author_facet | Kurnia Muludi Revita Setianingsih Ridho Sholehurrohman Akmal Junaidi |
| author_sort | Kurnia Muludi |
| collection | DOAJ |
| description | The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy. |
| first_indexed | 2024-04-24T16:28:27Z |
| format | Article |
| id | doaj.art-a7eef55e3d454b54b3a316e29eb9fc11 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T16:28:27Z |
| publishDate | 2024-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-a7eef55e3d454b54b3a316e29eb9fc112024-03-30T15:05:18ZengPeerJ Inc.PeerJ Computer Science2376-59922024-03-0110e196810.7717/peerj-cs.1968Exploiting nearest neighbor data and fuzzy membership function to address missing values in classificationKurnia Muludi0Revita Setianingsih1Ridho Sholehurrohman2Akmal Junaidi3Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung Province, IndonesiaComputer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, IndonesiaComputer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, IndonesiaComputer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, IndonesiaThe accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.https://peerj.com/articles/cs-1968.pdfMissing valuesClassification accuracyNearest neighbor dataFuzzy membership functionFuzzy logic |
| spellingShingle | Kurnia Muludi Revita Setianingsih Ridho Sholehurrohman Akmal Junaidi Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification PeerJ Computer Science Missing values Classification accuracy Nearest neighbor data Fuzzy membership function Fuzzy logic |
| title | Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification |
| title_full | Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification |
| title_fullStr | Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification |
| title_full_unstemmed | Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification |
| title_short | Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification |
| title_sort | exploiting nearest neighbor data and fuzzy membership function to address missing values in classification |
| topic | Missing values Classification accuracy Nearest neighbor data Fuzzy membership function Fuzzy logic |
| url | https://peerj.com/articles/cs-1968.pdf |
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