Abstract:

Partial Label Regression （PLR） complements the current situation that Partial Label Learning （PLL） only focuses on classification tasks. To address the problem that the existing PLR algorithms ignore characteristic differences between instance features， a Partial Label Regression algorithm integrating Feature Attention and Residual Connection （PLR-FARC） was proposed. Firstly， labels of real datasets were expanded into a set of real-value candidate labels by label enhancement technique. Secondly， the attention mechanism was employed to generate contribution of individual features to labels automatically. Thirdly， the residual connection was introduced to reduce information loss and maintain feature integrity during feature transmission. Finally， prediction loss was calculated based on IDent （IDentification method） and PIDent （Progressive IDentification method）， respectively. Experimental results on Abalone， Airfoil， Concrete， Cpu-act， Housing and Power-plant datasets show that compared to IDent and PIDent， PLR-FARC has the Mean Absolute Error （MAE） reduced by 2.15%， 38.38%， 8.86%， 4.19%， 15.71% and 15.55%， averagely and respectively， and the Mean Squared Error （MSE） reduced by 9.35%， 71.32%， 23.10%， 20.17%， 27.22% and 9.46%， averagely and respectively. It can be seen that the proposed algorithm is feasible and effective.

Key words: Partial Label Learning (PLL), Partial Label Regression (PLR), candidate label, attention mechanism, residual connection

摘要：

偏标签回归（PLR）弥补了偏标签学习（PLL）仅聚焦于分类任务的局限。针对现有的PLR算法忽略实例特征的特性差异的问题，提出一种集成特征注意力和残差连接的偏标签回归算法（PLR-FARC）。首先，通过标签增强技术将真实数据集的标签扩充为一组实值候选标签；其次，借助注意力机制自动生成每个特征对标签的贡献度；再次，引入残差连接以减少特征在传递过程中的信息丢失，从而维持特征的完整性；最后，分别基于IDent （IDentification method）和PIDent （Progressive IDentification method）计算预测损失。在Abalone、Airfoil、Concrete、Cpu-act、Housing和Power-plant数据集上的实验结果表明，相较于IDent和PIDent，PLR-FARC的平均绝对误差（MAE）分别平均降低了2.15%、38.38%、8.86%、4.19%、15.71%和15.55%，均方误差（MSE）分别平均降低了9.35%、71.32%、23.10%、20.17%、27.22%和9.46%。可见，所提算法是可行且有效的。

关键词: 偏标签学习, 偏标签回归, 候选标签, 注意力机制, 残差连接