Non-Linearity Incremental Ensemble Learning Based Depth Explorations of Latent Individual Contributions

IF 0.7 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiaozheng Deng, Yuanyuan Zhang, Shasha Mao, Peng Liu
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引用次数: 0

Abstract

In ensemble learning, individual accuracy and diversities are two key factors for improving the ensemble performance, and most existing methods have been designed from the view of boosting individual diversities. Whereas stronger individual diversities inevitably result in decreasing accuracies of partial individuals, it brings a great challenge for improving the ensemble performance. In fact, we observe that the contribution of individuals should be a crucial factor for ensemble learning, which can effectively alleviate the problem of balancing diversity and accuracy, but it is ignored in most existing studies. Based on this, we propose a new incremental non-linearity deep ensemble learning method that effectively combines multiple individuals based on the exploration of individual contributions by utilizing deep learning. In the proposed method, a characterization matrix is first constructed to represent the original individuals. Then, a deep ensemble network is constructed to explore the potential contribution of individuals in conjunction with the optimization objective of ensemble learning. Interestingly, a special layer is designed to achieve the minimization of ensemble error. Finally, experimental results on public datasets illustrate that the proposed method achieves the average 0.66–4.8% performance improvements compared to existing typical ensemble methods.

Abstract Image

基于非线性增量集成学习的潜在个体贡献深度探索
在集成学习中,个体的准确性和多样性是提高集成性能的两个关键因素,现有的大多数方法都是从提高个体多样性的角度来设计的。个体多样性的增强不可避免地导致部分个体精度的降低,这对集成性能的提高提出了很大的挑战。事实上,我们观察到个体的贡献应该是集成学习的一个关键因素,它可以有效地缓解多样性和准确性的平衡问题,但在大多数现有研究中被忽视。在此基础上,我们提出了一种新的增量非线性深度集成学习方法,该方法利用深度学习在探索个体贡献的基础上有效地组合了多个个体。在该方法中,首先构建表征矩阵来表示原始个体。然后,结合集成学习的优化目标,构建一个深度集成网络来探索个体的潜在贡献。有趣的是,设计了一个特殊的层来实现集成误差的最小化。最后,在公共数据集上的实验结果表明,与现有典型集成方法相比,该方法的性能平均提高了0.66 ~ 4.8%。
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来源期刊
Electronics Letters
Electronics Letters 工程技术-工程:电子与电气
CiteScore
2.70
自引率
0.00%
发文量
268
审稿时长
3.6 months
期刊介绍: Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews. Scope As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below. Antennas and Propagation Biomedical and Bioinspired Technologies, Signal Processing and Applications Control Engineering Electromagnetism: Theory, Materials and Devices Electronic Circuits and Systems Image, Video and Vision Processing and Applications Information, Computing and Communications Instrumentation and Measurement Microwave Technology Optical Communications Photonics and Opto-Electronics Power Electronics, Energy and Sustainability Radar, Sonar and Navigation Semiconductor Technology Signal Processing MIMO
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