| 摘要: |
| 作为一种解决标签模糊性问题的新学习范式, 标记分布学习(LDL)近年来受到了广泛的关注. 为了进一步提升标记分布学习的预测性能, 提出一种联合深度森林与异质集成的标记分布学习方法(LDLDF). 所提方法采用深度森林的级联结构模拟具有多层处理结构的深度学习模型, 在级联层中组合多个异质分类器增加集成的多样性. 相较于其他现有LDL方法, LDLDF能够逐层处理信息, 学习更好的特征表示, 挖掘数据中丰富的语义信息, 具有强大的表示学习能力和泛化能力. 此外, 考虑到深层模型可能出现的模型退化问题, LDLDF采用一种层特征重用机制(layer feature reuse)降低模型的训练误差, 有效利用深层模型每一层的预测能力. 大量的实验结果表明, 所提方法优于近期的同类方法. |
| 关键词: 标记分布学习 深度森林 深度集成模型 异质集成学习 特征重用 |
| DOI：10.13328/j.cnki.jos.006936 |
| 分类号:TP18 |
| 基金项目:国家重点基础研究发展计划(2020AAA0109602) |
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| Label Distribution Learning Method Based on Deep Forest and Heterogeneous Ensemble |
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WANG Yi-Fei, ZHU Ji-Hua, LIU Xin-Yuan, ZHOU Yi-Yang
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School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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| Abstract: |
| As a new learning paradigm to solve the problem of label ambiguity, label distribution learning (LDL) has received much attention in recent years. To further improve the prediction performance of LDL, this study proposes an LDL based on deep forest and heterogeneous ensemble (LDLDF), which uses the cascade structure of deep forest to simulate deep learning models with multi-layer processing structure and combines multiple heterogeneous classifiers in the cascade layer to increase the diversity of ensemble. Compared with other existing LDL methods, LDLDF can process information layer by layer and learn better feature representations to mine rich semantic information in data, and it has better representation learning ability and generalization ability. In addition, by considering the degradation problem of deep models, LDLDF adopts a layer feature reuse mechanism to reduce the training error of the model, which effectively utilizes the prediction ability of each layer in the deep model. Sufficient experimental results show that LDLDF is superior to other methods. |
| Key words: label distribution learning (LDL) deep forest deep ensemble model heterogeneous ensemble learning feature reuse |
