A new model for iris classification based on Naïve Bayes grid parameters optimization
Keywords:
Data mining, Classification, Naïve Bayes Classifier, Grid optimization, Accuracy.Abstract
Data mining classification plays an important role in the prediction of outcomes. One of the outstanding classifications methods in data mining is Naive Bayes Classification (NBC). It is capable of envisaging results and mostly effective than other classification methods. Many Naive Bayes classification method provide low performance in classification and regression problems Ones of the facts behinds the performances of the NBC is dues to the assumptions of contingent on independence amidst predictors and the initials hyper parameters. However, this strong assumption leads to loss of accuracy. In this study, a new method for boosting the accuracy of NBC was proposed. The proposed new technique uses a grid search to give better accuracy Naïve Bayes classification.
References
K. Netti and Y. Radhika, "A novel method for minimizing loss of accuracy in Naive Bayes classifier," in Computational Intelligence and Computing Research (ICCIC), 2015 IEEE International Conference on, 2015, pp. 1-4.
K. Netti and Y. Radhika, "An efficient Naïve Bayes classifier with negation handling for seismic hazard prediction," in Intelligent Systems and Control (ISCO), 2016 10th International Conference on, 2016, pp. 1-4.
T. Xiao, D. Ren, S. Lei, J. Zhang, and X. Liu, "Based on grid-search and PSO parameter optimization for Support Vector Machine," in Intelligent Control and Automation (WCICA), 2014 11th World Congress on, 2014, pp. 1529-1533.
L. Moore and C. Kambhampati, "The effect of features using Feature Selection for Bayesian Classifier," in Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, 2013, pp. 4641-4646.
M. Borrotti, G. Minervini, D. De Lucrezia, and I. Poli, "Naïve Bayes ant colony optimization for designing high dimensional experiments," Applied Soft Computing, vol. 49, pp. 259-268, 2016.
S. Cui, L. Zhao, Y. Wang, Q. Dong, J. Ma, Y. Wang, et al., "Using Naive Bayes Classifier to predict osteonecrosis of the femoral head with cannulated screw fixation," Injury, 2018.
T. S. Sujana, N. M. S. Rao, and R. S. Reddy, "An efficient feature selection using parallel cuckoo search and naïve Bayes classifier," in Networks & Advances in Computational Technologies (NetACT), 2017 International Conference on, 2017, pp. 167-172.
M. Duan, K. Li, X. Liao, and K. Li, "A parallel multiclassification algorithm for big data using an extreme learning machine," IEEE transactions on neural networks and learning systems, 2017.
M. Khalilinezhad, B. Minaei, G. Vernazza, and S. Dellepiane, "Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches," in Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2015, p. 94432G.
C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification," 2003.
L. Li and X.-L. Zhang, "Optimization of SVM with RBF kernel," Jisuanji Gongcheng yu Yingyong(Computer Engineering and Applications), vol. 42, pp. 190-192, 2006.
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