This study focuses on semi-supervised feature ranking (FR) applications for multi-target regression problems (MTR). As MTRs require prediction of several targets, we use a learning model that includes target interrelations via multi-objective trees. In processing the features for a semi-supervised learning model, transformation or scaling operations are usually required. To resolve this issue, we create a dissimilarity matrix via totally randomized trees to process the unsupervised information. Besides, we treat the split score function as a vector to make it suitable for considering each criterion regardless of their scales. We propose a semi-supervised FR scheme embedded to multi-objective trees that takes into account target and feature contributions simultaneously. Proposed FR score is compared with the state-of-the-art multi-target FR strategies via statistical analyses. Experimental studies show that proposed score significantly improves the performance of a recent tree-based and competitive multi-target learning model, i.e. predictive clustering trees. In addition, proposed approach outperforms its benchmarks when the available labelled data increase.

这项研究的重点是针对多目标回归问题（MTR）的半监督特征排名（FR）应用程序。由于MTR需要预测几个目标，因此我们使用一种学习模型，该模型包括通过多目标树进行的目标关联。在处理半监督学习模型的特征时，通常需要进行变换或缩放操作。为了解决这个问题，我们通过完全随机化的树创建一个不相似矩阵来处理无监督的信息。此外，我们将分割分数函数视为向量，以使其适合于考虑每个标准的规模。我们提出了一种嵌入到多目标树中的半监督FR方案，该方案同时考虑了目标和特征贡献。通过统计分析将建议的FR得分与最新的多目标FR策略进行比较。实验研究表明，建议分数显着提高了最近的基于树的竞争性多目标学习模型（即预测聚类树）的性能。此外，当可用的标记数据增加时，建议的方法将优于其基准。