Transfer learning by mapping and revising boosted relational dependency networks

Statistical machine learning algorithms usually assume the availability of data of considerable size to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data...

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Vydáno v:Machine learning Ročník 109; číslo 7; s. 1435 - 1463
Hlavní autoři: Azevedo Santos, Rodrigo, Paes, Aline, Zaverucha, Gerson
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2020
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computer Science
Control
Dependence
Domains
Machine Learning
Mapping
Mechatronics
Natural Language Processing (NLP)
Regression analysis
Robotics
Simulation and Modeling
Special Issue of the Inductive Logic Programming (ILP) 2019
Statistical analysis
Transfer learning
Statistical relational learning
Theory revision
ISSN:0885-6125, 1573-0565
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Shrnutí:Statistical machine learning algorithms usually assume the availability of data of considerable size to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data 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 contains objects and their relations, usually gathered from noisy environments. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning (SRL) area. Thus, to address domains with scarce, relational, and uncertain data , in this paper, we propose TreeBoostler, an algorithm that transfers the SRL state-of-the-art 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 additive trees into the target vocabulary. After, it employs two theory revision operators devised to handle incorrect relational regression trees aiming at improving the performance of the mapped trees. In the experiments presented in this paper, TreeBoostler has successfully transferred knowledge between several distinct domains. Moreover, it performs comparably or better than learning from scratch methods in terms of accuracy and outperforms a transfer learning approach in terms of accuracy and runtime.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-020-05871-x