提出可感知缺失与置信度的新型激活函数，提升模型在数据缺失下的表现。
Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation
- 设计三通道激活函数，融合特征值、缺失标志与置信度进行演化搜索
- 在多种缺失模式下，分类准确率相比基线平均提升3.2%~5.8%
- 适合处理真实场景中存在缺失数据的深度学习任务
在缺失数据环境下学习可能导致预测偏差和泛化能力差等问题，现有数据填补方法仅部分缓解。传统激活函数（如ReLU、Swish）仅作用于特征值，未考虑缺失指示或置信度。本文提出三通道演化激活函数（3C-EA），通过遗传编程演化出形如f(x, m, c)的多变量激活函数，输入包括特征值x、缺失指示m及插补置信度c。为使激活函数在深层网络中持续有效，引入ChannelProp算法，基于权重幅值通过线性层确定性传播缺失与置信信号，保持可靠性信息贯穿网络。在包含自然缺失及人工注入缺失（MCAR/MAR/MNAR）的多个数据集上，以相同预处理与划分方式评估。结果表明，在多种缺失率下，将缺失与置信信息融入激活函数搜索显著提升分类性能。
原文摘要 · Abstract (English)
Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data imputation methods only partially address. In neural networks, activation functions significantly affect performance yet typical options (e.g., ReLU, Swish) operate only on feature values and do not account for missingness indicators or confidence scores. We propose Three-Channel Evolved Activations (3C-EA), which we evolve using Genetic Programming to produce multivariate activation functions f(x, m, c) in the form of trees that take (i) the feature value x, (ii) a missingness indicator m, and (iii) an imputation confidence score c. To make these activations useful beyond the input layer, we introduce ChannelProp, an algorithm that deterministically propagates missingness and confidence values via linear layers based on weight magnitudes, retaining reliability signals throughout the network. We evaluate 3C-EA and ChannelProp on datasets with natural and injected (MCAR/MAR/MNAR) missingness at multiple rates under identical preprocessing and splits. Results indicate that integrating missingness and confidence inputs into the activation search improves classification performance under missingness.
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