Iris Classification with Supervised ML using Algorithm of KNN in JavaScript
Keywords:
Iris, KNN, Classification, Supervised, Data, Machine Learning, Algorithm
Abstract
The Machine learning is what we have to predict the data of unseen nature and gives us predicted results. There are number different machine learning algorithms for prediction and here we will use the supervised machine learning algorithm k-nearest neighbors (KNN) which is used for both the classification and regression analysis problems. In this paper we are predicting the Iris. Iris species are of three types and here we are going to predict that using k-nearest neighbors (KNN) algorithm model with the help of JavaScript.
References
[1] Taneja, Shweta, Charu Gupta, Kratika Goyal, and Dharna Gureja. "An enhanced k-nearest neighbor algorithm using information gain and clustering." In 2014 Fourth International Conference on Advanced Computing & Communication Technologies, pp. 325-329. IEEE, 2014.
[2] Thirunavukkarasu, K., Ajay S. Singh, Prakhar Rai, and Sachin Gupta. "Classification of IRIS dataset using classification based KNN algorithm in supervised learning." In 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1-4. IEEE, 2018.
[3] Su, Benyu, Reza Malekian, Jingcun Yu, Xihui Feng, and Zhixin Liu. "Electrical anisotropic response of water conducted fractured zone in the mining goaf." IEEE Access 4 (2016): 6216-6224.
[4] Abushmmala, Faten F., and Heba A. Abughali. "Color harmony classification using machine learning algorithms: KNN and SVM." In 2020 International Conference on Promising Electronic Technologies (ICPET), pp. 150-154. IEEE, 2020.
[5] Prihandi, Ifan. "KNN on Iris Data with Python Programming."
[6] Riquelme, Javier A., Ricardo J. Barrientos, Ruber Hernández-García, and Cristóbal A. Navarro. "An exhaustive algorithm based on GPU to process a kNN query." In 2020 39th International Conference of the Chilean Computer Science Society (SCCC), pp. 1-8. IEEE, 2020.
[7] Guo, Ying, Zeng-yuan Li, Er-xue Chen, and Xu Zhang. "The study of parallel KNN in the identification of forest type based on multi-spectral data." In 2011 International Conference on Computer Science and Service System (CSSS), pp. 4113-4115. IEEE, 2011.
[8] SADEQ, SADEER, KHAMIS A. ZIDAN, and JANE J. STEPHAN. "A Review on Iris Recognition System Based Different Classification Techniques." Solid State Technology 64, no. 2 (2021): 5106-5120.
[2] Thirunavukkarasu, K., Ajay S. Singh, Prakhar Rai, and Sachin Gupta. "Classification of IRIS dataset using classification based KNN algorithm in supervised learning." In 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1-4. IEEE, 2018.
[3] Su, Benyu, Reza Malekian, Jingcun Yu, Xihui Feng, and Zhixin Liu. "Electrical anisotropic response of water conducted fractured zone in the mining goaf." IEEE Access 4 (2016): 6216-6224.
[4] Abushmmala, Faten F., and Heba A. Abughali. "Color harmony classification using machine learning algorithms: KNN and SVM." In 2020 International Conference on Promising Electronic Technologies (ICPET), pp. 150-154. IEEE, 2020.
[5] Prihandi, Ifan. "KNN on Iris Data with Python Programming."
[6] Riquelme, Javier A., Ricardo J. Barrientos, Ruber Hernández-García, and Cristóbal A. Navarro. "An exhaustive algorithm based on GPU to process a kNN query." In 2020 39th International Conference of the Chilean Computer Science Society (SCCC), pp. 1-8. IEEE, 2020.
[7] Guo, Ying, Zeng-yuan Li, Er-xue Chen, and Xu Zhang. "The study of parallel KNN in the identification of forest type based on multi-spectral data." In 2011 International Conference on Computer Science and Service System (CSSS), pp. 4113-4115. IEEE, 2011.
[8] SADEQ, SADEER, KHAMIS A. ZIDAN, and JANE J. STEPHAN. "A Review on Iris Recognition System Based Different Classification Techniques." Solid State Technology 64, no. 2 (2021): 5106-5120.
Published
2023-01-31
How to Cite
Hassan, D., Qadir, A., & Hassan, B. (2023). Iris Classification with Supervised ML using Algorithm of KNN in JavaScript. International Journal of Artificial Intelligence & Mathematical Sciences, 1(2), 12-15. https://doi.org/10.58921/ijaims.v1i2.37
