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中南大学学报(英文版)

Journal of Central South University

Vol. 28    No. 6    June 2021

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Rotation forest based on multimodal genetic algorithm
XU Zhe(徐喆)1, 3, NI Wei-chen(倪维晨)2, JI Yue-hui(吉月辉)1, 3

1. School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300384, China;
2. Academic Affairs Office, Tianjin University of Technology, Tianjin 300384, China;
3. Tianjin Key Laboratory for Control Theory & Applications in Complicated Industry Systems,
Tianjin 300384, China

Abstract:In machine learning, randomness is a crucial factor in the success of ensemble learning, and it can be injected into tree-based ensembles by rotating the feature space. However, it is a common practice to rotate the feature space randomly. Thus, a large number of trees are required to ensure the performance of the ensemble model. This random rotation method is theoretically feasible, but it requires massive computing resources, potentially restricting its applications. A multimodal genetic algorithm based rotation forest (MGARF) algorithm is proposed in this paper to solve this problem. It is a tree-based ensemble learning algorithm for classification, taking advantage of the characteristic of trees to inject randomness by feature rotation. However, this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method. The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets. Experimental results show that the MGARF method outperforms the other methods, and the number of base learners in MGARF models is much fewer.

 

Key words: ensemble learning; decision tree; multimodal optimization; genetic algorithm

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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