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Ensemble Learning Based on Multimodal Multiobjective Optimization

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1159)

Abstract

In ensemble learning, the accuracy and diversity are two conflicting objectives. As the number of base learners increases, the prediction speed of ensemble learning machines drops significantly and the required storage space also increases rapidly. How to balance these two goals for selective ensemble learning is an extremely essential problem. In this paper, ensemble learning based on multimodal multiobjective optimization is studied in detail. The great significance and importance of multimodal multiobjective optimization algorithm is to find these different classifiers ensemble by considering the balance between accuracy and diversity, and different classifiers ensemble correspond to the same accuracy and diversity. Experimental results show that multimodal multiobjective optimization algorithm can find more ensemble combinations than unimodal optimization algorithms.

Keywords

Selective ensemble learning Ensemble learning Multimodal multiobjective optimization Learner diversity 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61976237,61922072, 61876169, 61673404).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
  2. 2.School of Electronic and Information EngineeringZhongyuan University of TechnologyZhengzhouChina

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