Transfer learning by mapping and revising boosted relational dependency networks

Abstract

Statistical machine learning algorithms usually assume that there is considerablysize data to train the models. However, traditional approaches fail to address domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of data scarcity by relying on a model learned in a source domain where data is easy to obtain to be a starting point for the target domain. On the other hand, real-world data is composed of objects and their relations usually disposed of in a noisy environment. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning area. To address these issues, scarce, relational, and uncertain data, in this work we propose TreeBoostler, an algorithm that transfers Boosted Relational Dependency Networks learned in a source domain to the target domain. TreeBoostler first finds a mapping between pairs of predicates to accommodate the trees in the target vocabulary. Then, it employs two novel theory revision operators devised to change relational regression trees to handle incorrectness and improve the performance of the mapped trees. TreeBoostler has successfully transferred knowledge among several distinct domains. It performs comparably or better than learning from scratch methods in terms of accuracy and outperforms an existing transfer learning approach in terms of accuracy and runtime.