Incremental k most similar neighbor classifier for mixed data
This paper presents an incremental k-most similar neighbor classifier, for mixed data and similarity functions that are not necessarily distances. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k most similar neighbors processed until s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2013-08-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
| Subjects: | |
| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/16307 |
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| author | Guillermo Sánchez-Díaz Uriel E. Escobar-Franco Luis R. Morales-Manilla Iván Piza-Dávila Carlos Aguirre-Salado Anilu Franco-Arcega |
| author_facet | Guillermo Sánchez-Díaz Uriel E. Escobar-Franco Luis R. Morales-Manilla Iván Piza-Dávila Carlos Aguirre-Salado Anilu Franco-Arcega |
| author_sort | Guillermo Sánchez-Díaz |
| collection | DOAJ |
| description |
This paper presents an incremental k-most similar neighbor classifier, for mixed data and similarity functions that are not necessarily distances. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k most similar neighbors processed until step t, traversing only once the training data set. Several experiments with synthetic and real data are presented.
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| first_indexed | 2024-04-09T22:07:41Z |
| format | Article |
| id | doaj.art-de54615b91ff488cb0e5a7abf9884a57 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:07:41Z |
| publishDate | 2013-08-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-de54615b91ff488cb0e5a7abf9884a572023-03-23T12:34:28ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442013-08-016710.17533/udea.redin.16307Incremental k most similar neighbor classifier for mixed dataGuillermo Sánchez-Díaz0Uriel E. Escobar-Franco1Luis R. Morales-Manilla2Iván Piza-Dávila3Carlos Aguirre-Salado4Anilu Franco-Arcega5Autonomous University of San Luis PotosiUniversidad Politécnica de TulancingoUniversidad Politécnica de TulancingoWestern Institute of Technology and Higher StudiesAutonomous University of San Luis PotosiAutonomous University of the State of Hidalgo This paper presents an incremental k-most similar neighbor classifier, for mixed data and similarity functions that are not necessarily distances. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k most similar neighbors processed until step t, traversing only once the training data set. Several experiments with synthetic and real data are presented. https://revistas.udea.edu.co/index.php/ingenieria/article/view/16307 supervised classificationincremental algorithmsartificial intelligencepattern recognition |
| spellingShingle | Guillermo Sánchez-Díaz Uriel E. Escobar-Franco Luis R. Morales-Manilla Iván Piza-Dávila Carlos Aguirre-Salado Anilu Franco-Arcega Incremental k most similar neighbor classifier for mixed data Revista Facultad de Ingeniería Universidad de Antioquia supervised classification incremental algorithms artificial intelligence pattern recognition |
| title | Incremental k most similar neighbor classifier for mixed data |
| title_full | Incremental k most similar neighbor classifier for mixed data |
| title_fullStr | Incremental k most similar neighbor classifier for mixed data |
| title_full_unstemmed | Incremental k most similar neighbor classifier for mixed data |
| title_short | Incremental k most similar neighbor classifier for mixed data |
| title_sort | incremental k most similar neighbor classifier for mixed data |
| topic | supervised classification incremental algorithms artificial intelligence pattern recognition |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/16307 |
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