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Li, L. (2017). Integration of information security and network data mining technology in the era of big data. Acta Technica CSAV (Ceskoslovensk Akademie Ved), 62(1), 157-165.Search in Google Scholar
Zheng, Q., Li, Y., & Cao, J. (2020). Application of data mining technology in alarm analysis of communication network. Computer Communications, 163(8).Search in Google Scholar
Yang, ZH, Pan, HX, FK, & Tang, et al. (2017). Research on electronic commerce information management system based on data mining. AGRO FOOD IND HI TEC.Search in Google Scholar
Poleto, T., Diogho, H. D. C. V., & Costa, A. P. C. S. (2017). The full knowledge of big data in the integration of inter-organizational information: an approach focused on decision making. International Journal of Decision Support System Technology, 9(1), 16-31.Search in Google Scholar
Atkinson-Abutridy, J., Mellish, C., & Aitken, S. (2017). Combining information extraction with genetic algorithms for text mining. IEEE Intelligent Systems, 19(3), 22-30.Search in Google Scholar
Karachi, A., Dezfuli, M. G., & Haghjoo, M. S. (2017). Intelligent information and database systems. Lecture Notes in Computer Science, 5990(6), 891-6.Search in Google Scholar
Saremi, M., & Yaghmaee, F. (2017). Improving evolutionary decision tree induction with multi‐interval Discretization. Computational Intelligence, 34(2), 495-514.Search in Google Scholar
Lomax, Susan, Vadera, & Sunil. (2017). A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework. The Computer Journal.Search in Google Scholar
Qiu, C., Jiang, L., & Li, C. (2017). Randomly selected decision tree for test-cost sensitive learning. Applied Soft Computing, 53, 27-33.Search in Google Scholar
Nicholas, J., Brownin, Raghunathan, Ramakrishnan, & O., et al. (2017). Genetic optimization of training sets for improved machine learning models of molecular properties. The Journal of Physical Chemistry Letters, 8(7), 1351-1359.Search in Google Scholar
Castaldo, D., Rosa, M., & Corni, S. (2021). Quantum optimal control with quantum computers: a hybrid algorithm featuring machine learning optimization. Physical Review A, 103.Search in Google Scholar
Noem- DeCastro-Garc-aángel LuisMuoz CastaedaDavid Escudero Garc-aMiguel V. Carriegos. (2019). Effect of the sampling of a dataset in the hyperparameter optimization phase over the efficiency of a machine learning algorithm. Complexity(3).Search in Google Scholar
Calvet, L., Jésica de Armas, Masip, D., & Juan, A. A. (2017). Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Mathematics, 15(1), 261-280.Search in Google Scholar
Ling, Q. H., Song, Y. Q., Han, F., Zhou, C. H., & Lu, H. (2018). An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity. Cognitive Systems Research, S1389041717302929.Search in Google Scholar
Zhang, X., Tian, Y., Cheng, R., & Jin, Y. (2018). A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Transactions on Evolutionary Computation, 22(99), 97-112.Search in Google Scholar
Moorthy, U., & Gandhi, U. D. (2020). Forest optimization algorithm-based feature selection using classifier ensemble. Computational Intelligence, 36.Search in Google Scholar
Danyang, W., & Fangming, S. (2020). Research of neural network structural optimization based on information entropy. Chinese Journal of Electronics.Search in Google Scholar
Sun, H., Hu, X., & Zhang, Y. (2019). Attribute selection based on constraint gain and depth optimal for a decision tree. Entropy, 21(2), 198.Search in Google Scholar