Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.

中文翻译：

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同质和异质空间中基于模糊规则的域适应


域自适应旨在利用从相关域（称为源域）获取的知识来提高当前域（称为目标域）中完成预测任务（分类或回归）的效率，当前域具有与目标域不同的概率分布。源域。尽管域适应已被广泛研究，但大多数现有研究都集中在同质域适应上，其中两个域具有相同的特征空间。最近，该领域提出的一个新挑战是概率分布和特征空间都不同的异构域适应。此外，在同构和异构领域适应中，分类任务取得了最大的努力和重大成就，而解决回归问题的成功解决方案却很有限。本文提出了两种创新的基于模糊规则的方法来处理回归问题。第一种方法称为模糊同质域适应，处理同质空间，而第二种方法称为模糊异质域适应，处理异质空间。首先通过学习过程从源域生成模糊规则；然后，通过建立潜在特征空间，将这些规则（也称为知识）转移到目标域，以最小化两个域特征空间之间的差距。通过对合成数据集的实验，我们证明了两种方法的有效性，并讨论了一些影响性能的重要参数的影响。对现实数据集的实验还表明，所提出的方法比现有源模型或使用少量目标数据构建的模型提高了目标模型的性能。