In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common.

中文翻译：

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在线半监督多目标回归的增量预测聚类树

在许多应用程序设置中，标记数据示例是一项昂贵的工作，而未标记的示例丰富且成本低廉。标记示例在在线环境中可能特别成问题，在这种环境中，可以有任意多的示例以高频率到达。当我们需要预测复杂值（例如，多个真实值）时，这也是有问题的，这项任务已经开始受到相当多的关注，但主要是在批处理设置中。在本文中，我们提出了一种在线半监督多目标回归的方法。它基于用于多目标回归的增量树和预测聚类框架。此外，与仅使用标记示例相比，它利用未标记的示例来提高其预测性能。我们将提出的 iSOUP-PCT 方法与监督树方法进行比较，不使用未标记的示例，以及使用未标记的示例的 oracle 方法，就好像它们被标记了一样。此外，我们将所提出的方法与可用的最先进方法进行比较。由于与其监督变体相比计算资源消耗增加，该方法实现了良好的预测性能。在标记示例非常少的情况下，所提出的方法在性能方面也击败了最新技术，同时在标记示例更常见时实现了可比的性能。由于与其监督变体相比计算资源消耗增加，该方法实现了良好的预测性能。在标记示例非常少的情况下，所提出的方法在性能方面也击败了最新技术，同时在标记示例更常见时实现了可比的性能。由于与其监督变体相比计算资源消耗增加，该方法实现了良好的预测性能。在标记示例非常少的情况下，所提出的方法在性能方面也击败了最新技术，同时在标记示例更常见时实现了可比的性能。