融合模糊图与多视图学习的快速分类模型
Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
- 用直觉模糊集处理数据不确定性，增强抗噪能力
- 通过图嵌入保留数据拓扑结构，提升泛化性能
- 适用于存在噪声和多源特征的复杂分类任务
随机向量函数链接（RVFL）网络因训练速度快和具备通用逼近能力而受到青睐。然而，传统RVFL模型在保持几何关系和有效利用多特征视图方面仍存挑战。为此，本文提出直觉模糊图嵌入的多视图学习随机向量函数链接模型（IFGRVFL-MV）。该模型包含三个核心组件：直觉模糊集用于处理不确定性，图嵌入用于捕捉内在几何结构，多视图学习用于融合多个特征空间的互补信息。模型为数据点分配直觉模糊隶属度与非隶属度，增强了对异常值的鲁棒性；图嵌入框架保留了数据的拓扑结构，提升了泛化性能。在UCI和KEEL基准数据集上的实验表明，IFGRVFL-MV在分类准确率上优于现有模型。结果验证了该方法在不确定性和多视图环境下的有效性。
原文摘要 · Abstract (English)
Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing multiple feature views effectively. To address these limitations we propose the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model. The proposed approach comprises three key components: intuitionistic fuzzy sets for uncertainty handling, graph embedding to capture intrinsic geometric structures, and multiview learning to use complementary information from multiple feature spaces. The model assigns intuitionistic fuzzy membership and non-membership values to data points making it robust to outliers. Also, the graph embedding framework preserves topological structures, increasing the generalization performance. We performed experiments on benchmark datasets from UCI and KEEL repositories which concludes that IFGRVFL-MV outperforms existing models in classification accuracy. Our results establish that IFGRVFL-MV is a promising advancement in the domain of uncertainty and multiview environments.
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