EVALUATION OF THE K-NEAREST NEIGHBOR MODEL WITH K-FOLD CROSS VALIDATION ON IMAGE CLASSIFICATION
Keywords:
Machine learning, k-NN, k-FCV, Image Processing, Classification.
Abstract
In this paper, the data used is the banana image which is extracted into the dataset into 4 attributes, namely red, green, blue, and the mean for the classification process. Image data is classified using the k-Nearest Neighbor method which will be optimized the model with the k-Fold Cross Validation algorithm. Evaluation of the k-NN model with the k-FCV algorithm can improve accuracy and can build better machine learning models in the image classification process. The default K-NN obtained an accuracy rate of 57%, while the results of the model evaluation with the k-FCV algorithm, on fold 3 obtained an accuracy rate of 68%. The percentage yield with the new model increased by 11% which indicates that the machine learning model that was built was quite optimal
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References
[1] M.R. Wayahdi, Tulus, & M.S. Lydia, “Combination of k-Means with Naïve Bayes Classifier in the Process of Image Classification”, IOP Conference Series: Materials Science and Engineering, 3rd NICTE, pp. 1-7, 2020.
[2] Z. Chen & B. Liu, “Lifelong Machine Learning (Second Edition)”, Handbook Morgan and Claypool Publisher, pp. 1-187, 2018.
[3] M. Kang & N.J. Jameson, “Machine Learning: Fundamentals.”, Handbook Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and Internet of Things, pp. 85-109, 2018.
[4] A. Nayak & K. Dutta, “Impacts of Machine Learning and Artificial Intelligence on Mankind”, International Conference on Intelligent Computing and Control (12C2), pp. 1-3, 2017.
[5] D. Pan, Z. Zhao, L. Zhan, & C. Tang, “Recursive Clustering k-Nearest Neighbors Algorithm and the Application in the Classification of Power Quality Disturbance, IEEE Confrence on Energy Internet and Energy System Integration (EI2), pp. 1-5, 2017.
[6] V.L. Boiculese, G. Dimitru, & M. Moscalu, “Improving Recall of k-Nearest Neighbor Algorithm for Classes of Uneven Size”, the 4th IEEE International Conference on E-Healt and Bioengineering-EHB, pp. 1-4, 2013.
[7] S. Du, & J. Li, “Parallel Processing of Improved k-NN Text Classification Algorithm Based on Hadoop”, IEEE 7th International Conference on Information, Communication, and Networks, pp. 167-170, 2019.
[8] W. Lei & L. Zhaowei, “Research on the Humanlike Trajectories Control of Robots Based on the k-Nearest Neighbors”, IEEE Chinese Automation Congress (CAC), pp. 7746-7751, 2017.
[9] M. Kang, “Machine Learning: Anomaly Detection”, Handbook Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and Internet of Things, pp. 131-162, 2018.
[10] H. Jaafar, N. Mukahar, & D.A. Ramli, “Methodology of Nearest Neighbor: Design and Comparison of Biometric Image Database”, IEEE Student Conference on Research and Development (SCOReD), pp. 1-6, 2016.
[11] G. Li & J. Zhang, “Music Personalized Recommendation System Based on Improved KNN Algorithm”, IEEE 3rd Advanced Information Technology, Electronic, and Automation Control Conference (IAEAC), pp. 777-781, 2018.
[12] P. Tamilarasi & U. Riani, “Diagnosis of Crime Rate Against Women using k-Fold Cross Validation through Machine Learning Algorithms”, IEEE 4th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1034-1038, 2020.
[13] B. Mu, T. Chen, & L. Ljung, “Asymptotic Properties of Hyperparameter Estimators by using Cross-Validation for Regularized System Identification”, IEEE Confrence on Decision and Control (CDC), pp. 644-649, 2018.
[14] D.R.S. Caon, A. Amehraye, J. Razik, G. Chollet, R.V. Andreao, & C. Mokbel, “Experiments on Acoustic Model Supervised Adaptation and Evaluation by k-Fold Cross Validation Techique”, IEEE International Journal, pp. 1-4, 2010.
[2] Z. Chen & B. Liu, “Lifelong Machine Learning (Second Edition)”, Handbook Morgan and Claypool Publisher, pp. 1-187, 2018.
[3] M. Kang & N.J. Jameson, “Machine Learning: Fundamentals.”, Handbook Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and Internet of Things, pp. 85-109, 2018.
[4] A. Nayak & K. Dutta, “Impacts of Machine Learning and Artificial Intelligence on Mankind”, International Conference on Intelligent Computing and Control (12C2), pp. 1-3, 2017.
[5] D. Pan, Z. Zhao, L. Zhan, & C. Tang, “Recursive Clustering k-Nearest Neighbors Algorithm and the Application in the Classification of Power Quality Disturbance, IEEE Confrence on Energy Internet and Energy System Integration (EI2), pp. 1-5, 2017.
[6] V.L. Boiculese, G. Dimitru, & M. Moscalu, “Improving Recall of k-Nearest Neighbor Algorithm for Classes of Uneven Size”, the 4th IEEE International Conference on E-Healt and Bioengineering-EHB, pp. 1-4, 2013.
[7] S. Du, & J. Li, “Parallel Processing of Improved k-NN Text Classification Algorithm Based on Hadoop”, IEEE 7th International Conference on Information, Communication, and Networks, pp. 167-170, 2019.
[8] W. Lei & L. Zhaowei, “Research on the Humanlike Trajectories Control of Robots Based on the k-Nearest Neighbors”, IEEE Chinese Automation Congress (CAC), pp. 7746-7751, 2017.
[9] M. Kang, “Machine Learning: Anomaly Detection”, Handbook Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and Internet of Things, pp. 131-162, 2018.
[10] H. Jaafar, N. Mukahar, & D.A. Ramli, “Methodology of Nearest Neighbor: Design and Comparison of Biometric Image Database”, IEEE Student Conference on Research and Development (SCOReD), pp. 1-6, 2016.
[11] G. Li & J. Zhang, “Music Personalized Recommendation System Based on Improved KNN Algorithm”, IEEE 3rd Advanced Information Technology, Electronic, and Automation Control Conference (IAEAC), pp. 777-781, 2018.
[12] P. Tamilarasi & U. Riani, “Diagnosis of Crime Rate Against Women using k-Fold Cross Validation through Machine Learning Algorithms”, IEEE 4th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1034-1038, 2020.
[13] B. Mu, T. Chen, & L. Ljung, “Asymptotic Properties of Hyperparameter Estimators by using Cross-Validation for Regularized System Identification”, IEEE Confrence on Decision and Control (CDC), pp. 644-649, 2018.
[14] D.R.S. Caon, A. Amehraye, J. Razik, G. Chollet, R.V. Andreao, & C. Mokbel, “Experiments on Acoustic Model Supervised Adaptation and Evaluation by k-Fold Cross Validation Techique”, IEEE International Journal, pp. 1-4, 2010.
Published
2020-12-06
How to Cite
Wayahdi, M. R., Syahputra , D., & Hafiz Nanda Ginting , S. (2020). EVALUATION OF THE K-NEAREST NEIGHBOR MODEL WITH K-FOLD CROSS VALIDATION ON IMAGE CLASSIFICATION. INFOKUM, 9(1,Desember), 1-6. Retrieved from http://infor.seaninstitute.org/index.php/infokum/article/view/72
