A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function
https://doi.org/10.24017/Science.2022.2.5
Abstract views: 414 / PDF downloads: 164Abstract
There are many potential sources of data uncertainty, such as imperfect measurement or sampling, intrusive environmental monitoring, unreliable sensor networks, and inaccurate medical diagnoses. To avoid unintended results, data mining from new applications like sensors and location-based services needs to be done with care. When attempting to classify data with a high degree of uncertainty, many researchers have turned to heuristic approaches and machine learning (ML) methods. We propose an entirely new ML method in this paper by fusing the Radial Basis Function (RBF) network based on ant colony optimization (ACO). After introducing a large amount of uncertainty into a dataset, we normalize the data and finish training on clean data. The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). Error metrics show that our model significantly outperforms the gold standard and other popular ML methods. Using industry-standard performance metrics, the results of our experiments show that our proposed method does a better job of classifying uncertain data than other methods
Keywords:
Uncertainty data, Machine Learning, Radial Basis Function, Ant Colony Optimization (ACO), IoT Applications
References
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[2] V. Choudhary and J. Pranita, "Classification: A Decision Tree for Uncertain Data Using CDF, International Journal of Engineering Research and Applications," pp. 3(1): 1501-1506., 2013.
[3] S. Tsang, K. Ben, Y. Y. Kevin, H. Wai-Shing and D. L. Sau, "Decision Trees for Uncertain Data, IEEE Transactions On Knowledge And Data Engineering.," pp. 23(1):64-78, 2011.
https://doi.org/10.1109/TKDE.2009.175
[4] G. Suresh, S. Shaik, O. Reddy, & Munibhadrayya, B. "classification of uncertain data using fuzzy neural networks, world of computer science and information networks, world of computer science and informationTechnology Journal.," p. 1(4), 2011.
[5] Q. Biao, Y. Xia, Prabhakar and Y. Tu, "A Rule-Based Classification Algorithm for Uncertain Data, in proceedings of IEEE International Conference on.," 2009.
[6] J. Ge and X. Yuni, "UNN: A Neural Network for uncertain data classification," pp. 6118: 449-460., 2010.
https://doi.org/10.1007/978-3-642-13657-3_48
[7] J. Ren, D. L. Sau, C. Xianlu, K. Ben, C. Reynold and C. David, "Naive Bayes Classification of Uncertain Data, in proceedings of ninth IEEE International Conference on," 2009.
https://doi.org/10.1109/ICDM.2009.90
[8] W. Ngai, K. B., K. C. C., C. R. and Y. Chau, "Efficient clustering of uncertain data,Springer, Heidelberg," pp. 4065: 436-445., 2006.
https://doi.org/10.1109/ICDM.2006.63
[9] S. Singh, M. Chris, P. Sunil, S. Rahul and H. Susanne, "Indexing Uncertain Categorical Data, In Proc.of ICDE," pp. 616-625., 2007.
https://doi.org/10.1109/ICDE.2007.367907
[10] M. Mehta and A. Rakesh, "SLIQ- A Fast Scalable Classifier for Data Mining, In 5th Intl. Conf. on Extending Database Technology.," 1996.
https://doi.org/10.1007/BFb0014141
[11] L. Qinghua, X. Yan, J. Li and Y. Peng, "DDEUDSC: A Dynamic Distance Estimation using Uncertain Data Stream Clustering in mobile wireless sensor networks, Journal on Measurement," pp. 55: 423-433, 2014 .
https://doi.org/10.1016/j.measurement.2014.05.040
[12] M. Bounhas, G. Mohammad, P. Henri, S. Mathieu and M. Khaled, "Naive possibilistic classifiers for imprecise or uncertain numerical data, Journal on fuzzy sets and systems," pp. 239: 137-156, 2014.
https://doi.org/10.1016/j.fss.2013.07.012
[13] S. Kiran, R. M. Venugopal and P. N. Reddy, "Classification of uncertain data using decision trees, International Journal of Advanced Research in Computer Science and Software Engineering," pp. 3(10): 40- 46, 2013.
[14] L. Chunquan, Y. Zhang, P. Shi and Z. Hu, "Learning very fast decision tree from uncertain data streams with positive and unlabeled samples," Journal on Information Sciences , pp. 213: 50-67, 2012.
https://doi.org/10.1016/j.ins.2012.05.023
[15] Xu, Lei and H. Edward, "Improving classification accuracy on uncertain data by considering multiple subclasses, Journal on Neuro Computing," pp. 145: 98-107 , 2014.
https://doi.org/10.1016/j.neucom.2014.05.056
[16] B. Qina, X. Yuni, W. Shan and D. Xiaoyong, "A novel Bayesian classification for uncertain data, Journal on Knowledge-based System," pp. 24(8): 1151-1158., 2011. [17] A. Sadegh, "Uncertainty Avoider Interval Type II Defuzzification Method,Mathematical Problems in Engineering," 2020.
[18] C. S. K. Dash, A. K. Behera, S. Dehuri and S.-B. Cho, "Radial basis function neural networks: a topical stateof-the-art," p. 6:33-63, 2016.
https://doi.org/10.1515/comp-2016-0005
[19] R. Wang, D. Li and K. Miao, "Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles," 2020.
https://doi.org/10.3390/jmse8030210
[20] K. Ivan, H. Marta, V. Jan, K. Jan and L. Ondrej, "Radial Basis Function Neural NetworkBased Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems ,polymers," 2019.
[21] J. Kennedy and R. Eberhart, "Particle swarm optimization," in in Proceedings of the IEEE International Conference on Neural Networks,, 1995.
[22] E. Assareh, M. ,. Behrang, M. R. Assari and A. Ghanbarzadeh, "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," p. 5223-5229, 2010.
https://doi.org/10.1016/j.energy.2010.07.043
[23] S.R.. Mahmood, M. Hatami and Moradi, P., 2020, October. A Trust-based Recommender System by Integration of Graph Clustering and Ant Colony Optimization. In 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 598-604). IEEE.
https://doi.org/10.1109/ICCKE50421.2020.9303647
[24] C.W. Fisher, E.J. Lauria, and Matheus, C.C., 2009. An accuracy metric: Percentages, randomness, and probabilities. Journal of Data and Information Quality (JDIQ), 1(3), pp.1-21.
https://doi.org/10.1145/1659225.1659229
[25] G. Hripcsak, and A.S Rothschild, 2005. Agreement, the f-measure, and reliability in information retrieval. Journal of the American medical informatics association, 12(3), pp.296-298.
https://doi.org/10.1197/jamia.M1733
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How to Cite
[1]
T. A. Mohammed Amin, S. R. Mahmood, R. D. Mohammed, and P. J. Karim, “A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function”, KJAR, pp. 57–70, Nov. 2022, doi: 10.24017/Science.2022.2.5.
Published
27-11-2022
Issue
Section
Pure and Applied Science
