A New Feature Extraction Algorithm to Extract Differentiate Information and Improve KNN-based Model Accuracy on Aquaculture Dataset

Oskar Natan, Agus Indra Gunawan, Bima Sena Bayu Dewantara


In the world of aquaculture, understanding the condition of a pond is very important for a farmer in deciding which action should they take to prevent any bad condition occurred. Condition of a pond can be justified by measuring plenty of water parameters which can be divided into 3 categories that are physical, chemical and biological. The physical parameter is any physical quantity that can be measured in the pond. The chemical parameter is any kind of chemical substances that are dissolved in water. The biological parameter is any organic matter that lives in water. However, all of these parameters are not so distinguishable in representing the condition of a pond. Therefore, the farmer experience difficulties in justifying the condition and taking proper action to their pond. Even with the help of the K-Nearest Neighbors (KNN) algorithm combined with grid search optimization to model the data, the result is still not satisfying where the model only achieve accuracy of 0.701 in leave one out validation. To overcome this problem, a kind of feature extraction algorithm is needed to extract more information and make the data become more differentiate in representing the condition of the pond. With the help of our proposed feature extraction algorithm, optimized KNN can model the data easier and achieve higher accuracy. From the experiment results, the proposed feature extraction algorithm gives an impressive performance where it increases the accuracy to 0.741. A comparison with other feature extraction algorithms such as Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Singular Value Decomposition (SVD) is also conducted to validate how good the proposed feature extraction algorithm is. As a result, the proposed algorithm is surpassing the other algorithms which only achieve the accuracy of 0.707, 0.718, and 0.718, respectively.


feature extraction; algorithm; KNN; grid search; aquaculture

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Ruslisan, N. H. Kalam, A. C. Dwininta, M. H. Habibi, E. T. Rahayu, N. Dewi, E. E. Henny, and W. Widyatmanti, “Water quality assessment using remote sensing and GIS for in-shore marine environment suitability,†Aquacultura Indonesiana, vol. 17, pp. 46-53, 2016.

D. Yuswantoro, O. Natan, A. N. Angga, A. I. Gunawan, Taufiqurrahman, B. S. B. Dewantara, A. Kurniawan, “Fuzzy logic-based control system for dissolved oxygen control on indoor shrimp cultivation,†in Proc. International Electronics Symposium on Engineering Technology and Applications (IES-ETA), 2018, p. 37.

M. Muslim, M. Fitrani, and A. M. Afrianto, “The effect of water temperature on incubation period, hatching rate, normalities of the larvae and survival rate of snakehead fish channa striata,†Aquacultura Indonesiana, vol. 19, pp. 90-94, 2018.

Djumanto, Ustadi, Rustadi, and B. Triyatno, “Utilization of Wastewater from Vannamei Shrimp Pond for Rearing Milkfish in Keburuhan Coast Purworejo Sub-District,†Aquacultura Indonesiana, vol. 19 (1), pp. 38-46, 2018.

D. Ayon, “Machine Learning Algorithms: A Review,†International Journal of Computer Science and Information Technologies, vol. 7 (3), pp. 1174-1179, 2016.

M. Khadr and M. Elshemy, “Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt,†Ain Shams Engineering Journal, vol. 8, pp. 549-557, 2016.

L. Xu and S. Liu, “Study of short-term water quality prediction model based on wavelet neural network,†Mathematical and Computer Modelling, vol. 58, pp. 807–813, 2013.

G. Tan, J. Yan, C. Gao, and S. Yang, “Prediction of water quality time series data based on least squares support vector machine,†in Proc. International Conference on Advances in Computational Modeling and Simulation, 2012, p. 1194.

C. Deng, Y. Gao, J. Gu, X. Miao, and S. Li, “Research on the growth model of aquaculture organisms based on neural network expert system,†in Proc. 6th International Conference on Natural Computation, 2010, p. 1812.

I. Ahmad, M. Basheri, M. J. Iqbal and A. Rahim, “Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection,†IEEE Access, vol. 6, pp. 33789-33795, 2018.

D. Banik, A. Ekbal and P. Bhattacharyya, “Machine Learning Based Optimized Pruning Approach for Decoding in Statistical Machine Translation,†IEEE Access, vol. 7, pp. 1736-1751, 2019.

B.S.B. Dewantara and J. Miura, "Estimating Head Orientation using a Combination of Multiple Cues", IEICE Trans. on Information and Systems, vol. E99-D, no. 6, pp. 1603-1613, 2016.

F. Zhao and Q. Tang, “A KNN Learning Algorithm for Collusion-Resistant Spectrum Auction in Small Cell Networks,†IEEE Access, vol. 6, pp. 45796-45803, 2018.

A. Rojas-Domínguez, L. C. Padierna, J. M. Carpio Valadez, H. J. Puga-Soberanes and H. J. Fraire, “Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis,†IEEE Access, vol. 6, pp. 7164-7176, 2018.

J. Tong, J. Xi, Q. Guo and Y. Yu, “Low-complexity cross-validation design of a linear estimator,†Electronics Letters, vol. 53, no. 18, pp. 1252-1254, 2017.

D. M. W. Powers, “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation,†Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011.

B. N. Li, Q. Yu, R. Wang, K. Xiang, M. Wang and X. Li, “Block Principal Component Analysis With Nongreedy L1-Norm Maximization,†IEEE Transactions on Cybernetics, vol. 46, no. 11, pp. 2543-2547, 2016.

M. Å avc, V. Glaser, J. Kranjec, I. Cikajlo, Z. MatjaÄiÄ and A. Holobar, “Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms,†IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 10, pp. 1935-1944, 2018.

N. Halko, P. G. Martinsson, and J. A. Tropp, “Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions,†Society for Industrial and Applied Mathematics, vol. 53, pp. 217-288, 2011.

T. Yokota, N. Lee and A. Cichocki, “Robust Multilinear Tensor Rank Estimation Using Higher Order Singular Value Decomposition and Information Criteria,†IEEE Transactions on Signal Processing, vol. 65, no. 5, pp. 1196-1206, 2017.

DOI: http://dx.doi.org/10.18517/ijaseit.9.3.8041


Published by INSIGHT - Indonesian Society for Knowledge and Human Development