Abstract
Ensemble learning (EL) is a paradigm, involving several base learners working together to solve complex problems. The performance of the EL highly relies on the number and accuracy of weak learners, which are often hand-crafted by domain knowledge. Unfortunately, such knowledge is not always available to interested end-user. This paper proposes a novel approach to automatically select optimal type and number of base learners for disease classification, called Multi-Objective Evolutionary Ensemble Learning (MOE-EL). In the proposed MOE-EL algorithm, a variable-length gene encoding strategy of the multi-objective algorithm is first designed to search for the weak learner optimal configurations. Moreover, a dynamic population strategy is proposed to speed up the evolutionary search and balance the diversity and convergence of populations. The proposed algorithm is examined and compared with 5 existing algorithms on disease classification tasks, including the state-of-the-art methods. The experimental results show the significant superiority of the proposed approach over the state-of-the-art designs in terms of classification accuracy rate and base learner diversity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(4), e1249 (2018)
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., et al.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189–215 (2020)
Chen, S., Webb, G.I., Liu, L., et al.: A novel selective naïve Bayes algorithm. Knowl.-Based Syst. 192, 105361 (2020)
Heidari, A.A., Faris, H., Mirjalili, S., et al.: Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. Nat.-Inspired Optim. 23–46 (2020)
Zhou, K., Yang, Y., Qiao, Y., et al.: Domain adaptive ensemble learning. IEEE Trans. Image Process. 30, 8008–8018 (2021)
Zhou, Z.-H.: Ensemble learning. In: Machine Learning, pp. 181–210. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-1967-3_8
Bi, Y., Xue, B., Zhang, M.: An automated ensemble learning framework using genetic programming for image classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 365–373 (2019)
Ma, L., Huang, M., Yang, S., et al.: An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2020.3041212
Ma, L., Li, N., Guo, Y., et al.: Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3086501
Ma, L., Wang, X., Huang, M., et al.: Two-level master–slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer. IEEE Trans. Syst. Man Cybern. Syst. 49(5), 861–880 (2019)
Zhang, S., Chen, Y., Zhang, W., et al.: A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting. Inf. Sci. 544, 427–445 (2021)
Ma, M., Sun, C., Mao, Z., et al.: Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery. ISA Trans. 113, 166–174 (2021)
Zhang, C., Sun, J.H., Tan, K.C.: Deep belief networks ensemble with multi-objective optimization for failure diagnosis. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 32–37. IEEE (2015)
Chandra, A., Yao, X.: Ensemble learning using multi-objective evolutionary algorithms. J. Math. Model. Algorithms 5(4), 417–445 (2006)
Deb, K.: Multi-objective evolutionary algorithms. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer handbook of computational intelligence, pp. 995–1015. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-43505-2_49
Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Xue, Y., Li, Y.F.: Multi-objective integer programming approaches for solving optimal feature selection problem: a new perspective on multi-objective optimization problems in SBSE. In: Proceedings of the 40th International Conference on Software Engineering, pp. 1231–1242 (2018)
Ma, L., Li, N., Yu, G., et al.: How to simplify search: classification-wise pareto evolution for one-shot neural architecture search. arXiv preprint arXiv:2109.07582 (2021)
Karagoz, G.N., Yazici, A., Dokeroglu, T., et al.: A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data. Int. J. Mach. Learn. Cybern. 12(1), 53–71 (2021)
Chu, X., Zhang, B., Xu, R.: Multi-objective reinforced evolution in mobile neural architecture search. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12538, pp. 99–113. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66823-5_6
Wang, X., Hu, T., Tang, L.: A multiobjective evolutionary nonlinear ensemble learning with evolutionary feature selection for silicon prediction in blast furnace. IEEE Trans. Neural Netw. Learn. Syst. 33, 2080–2093 (2021)
Oza, N.C., Russell, S.: Online Ensemble Learning. University of California, Berkeley (2001)
Krawczyk, B., Minku, L.L., Gama, J., et al.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017)
Zhang, Y., Jin, X.: An automatic construction and organization strategy for ensemble learning on data streams. ACM SIGMOD Rec. 35(3), 28–33 (2006)
Zhang, X.: Automatic ensemble learning for online influence maximization. arXiv preprint arXiv:1911.10728 (2019)
Sun, Y., Xue, B., Zhang, M., et al.: Evolving deep convolutional neural networks for image classification. IEEE Trans. Evol. Comput. 24(2), 394–407 (2019)
Abdollahi, J., Nouri-Moghaddam, B.: Hybrid stacked ensemble combined with genetic algorithms for prediction of diabetes. arXiv preprint arXiv:2103.08186 (2021)
Ma, L., Cheng, S., Shi, Y.: Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans. Syst. Man Cybern. Syst. 51(11), 6723–6742 (2020)
Ma, L., Wang, X., Wang, X., et al.: TCDA: truthful combinatorial double auctions for mobile edge computing in industrial Internet of Things. IEEE Trans. Mob. Comput. (2021). https://doi.org/10.1109/TMC.2021.3064314
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, N., Ma, L., Zhang, T., He, M. (2022). Multi-objective Evolutionary Ensemble Learning for Disease Classification. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-09677-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09676-1
Online ISBN: 978-3-031-09677-8
eBook Packages: Computer ScienceComputer Science (R0)