Prasetyo, Eko and Purbaningtyas, Rani and Adityo, Raden (2020) Cosine K-Nearest Neighbor in Milkfish Eye Classification. International Journal of Intelligent Engineering and Systems, 13 (3). pp. 11-25. ISSN 21853118
Text (Artikel Cosine KNN)
INASS - Cosine KNN.pdf - Published Version Available under License Creative Commons Attribution Share Alike. Download (10MB) |
|
Text (Hasil similarity artikel Cosine KNN)
Similarity - Cosine K-Nearest Neighbor in Milkfish Eye Classification.pdf - Supplemental Material Available under License Creative Commons Attribution Share Alike. Download (4MB) |
|
Text (Hasil Peer Review)
1. Consine K Nearest Neighbor In Milkfish Eye Classification.pdf - Supplemental Material Restricted to Repository staff only Download (654kB) |
Abstract
K-Nearest Neighbors (K-NN) classification method gains refined version proposed by the researcher. The refinement aims to solve noise sensitive when using small K, and irrelevant class as classification result when using large K. The problem in the previous version of method was that the weights were calculated individually, so the result was not optimal. We propose recent weighting scheme where the weights were no longer gained from the nearest neighbor individually, but by involving all pair of the nearest neighbor, called Cosine K-NN (CosKNN). We also introduce a trigonometric map to describe the Cosine weight. CosKNN is soft value to represent ownership of each class to the testing data. Empirically, CosKNN is tested and compared with other K-NN refinement using milkfish eye, UCI, and KEEL dataset. The result shows that CosKNN hold superior performance compared to the other methods although K number is higher of which accuracy is 96.79%.
Item Type: | Article |
---|---|
Subjects: | 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 458 - Teknik Informatika |
Divisions: | Jurusan Teknologi Informasi > Prodi D4 Teknik Informatika > Publikasi |
Depositing User: | Rani Purbaningtyas |
Date Deposited: | 12 May 2023 08:46 |
Last Modified: | 17 Jun 2023 07:33 |
URI: | https://sipora.polije.ac.id/id/eprint/23114 |
Actions (login required)
View Item |