Abstract
The traditional random forest algorithm works along with unbalanced data, cannot achieve satisfactory prediction results for minority class, and suffers from the parameter selection dilemma. In view of this problem, this paper proposes an unbalanced accuracy weighted random forest algorithm (UAW_RF) based on the adaptive step size artificial bee colony optimization. It combines the ideas of decision tree optimization, sampling selection, and weighted voting to improve the ability of stochastic forest algorithm when dealing with biased data classification. The adaptive step size and the optimal solution were introduced to improve the position updating formula of the artificial bee colony algorithm, and then the parameter combination of the random forest algorithm was iteratively optimized with the advantages of the algorithm. Experimental results show satisfactory accuracies and prove that the method can effectively improve the classification accuracy of the random forest algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Raska, S., Ming, G., Ying, X., Tao, H.: Python Machine Learning. China Machine Press, Beijing (2017)
Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions In: International Conference on Machine Learning, San Diego, California, USA, pp. 96–103. ACM (2008)
Ren, C.: Urban PM (2.5) concentration prediction based on parallel random forest. Taiyuan University of Technology (2018)
Wu, Q., Li, Y., Zheng, X.: Stochastic forest algorithm optimization for unbalanced training set classification. Ind. Control. Comput. 26(7), 89–90 (2013)
Chang, Y., Sun, X., Zhong, L., Wang, F., Liu, Y.: State evaluation of industrial process operation based on Improved Stochastic Forest algorithm. Acta Autom. Sin., 1–10 (2019). https://doi.org/10.16383/j.aas.c190066
Xu, Y., Zhang, J., Gong, X., Jiang, K., Zhou, H., Yin, J.: Real time flow classification method of power business based on Improved Stochastic Forest algorithm. Power Syst. Prot. Control. 44(24), 82–89 (2016)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)
Zhang, Q., Li, P., Wang, M.: Optimization algorithm of artificial bee colony based on adaptive evolutionary strategy. J. Univ. Electron. Sci. Technol. 48(04), 560–566 (2019)
Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)
Zhou, T., Ming, D., Zhao, R.: Land cover classification by parameter optimization stochastic forest algorithm. Mapp. Sci. 42(02), 88–94 (2017)
Liu, B., Jiang, M., Zhang, Z.: Artificial bee colony algorithm based on tabu search and its application. Comput. Appl. Res. 32(07), 2005–2008 (2015)
Ma, A., Zhang, C., Zhang, B., Zhang, X.: An adaptive artificial swarm algorithm for classification problems. J. Jilin Univ. (Eng. Ed.) 46(01), 252–258 (2016)
Du, Z., Liu, G., Han, D., Yu, X., Jia, J.: Elite artificial swarm algorithm based on global unbiased search strategy. J. Electron. 46(02), 308–314 (2018)
Sankar, P., Vishwanath, N., Lang, H., et al.: An effective content based medical image retrieval by using ABC based artificial neural network (ANN). Curr. Med. Imaging Rev. 12(999), 1 (2016)
Luo, J., Liu, Q., Yang, Y., et al.: An artificial bee colony algorithm for multi-objective optimisation. Appl. Soft Comput. 50, 235–251 (2017)
Qian, X., Qin, J., Song, W.: Improved parallel stochastic forest algorithm and its out of package estimation. Comput. Appl. Res. 35(06), 1651–1654 (2018)
Acknowledgement
This work is supported by the CERNET Innovation Project (No. NGII20190315) and the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huo, J., Qin, X., Al-Neshmi, H.M.M., Mu, L., Ju, T. (2020). Improved Random Forest Algorithm Based on Adaptive Step Size Artificial Bee Colony Optimization. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-7984-4_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7983-7
Online ISBN: 978-981-15-7984-4
eBook Packages: Computer ScienceComputer Science (R0)