Transfer learning in constructive induction with Genetic Programming
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Transfer learning in constructive induction with Genetic Programming Luis Muñoz1 · Leonardo Trujillo1 · Sara Silva2 Received: 27 January 2019 / Revised: 19 October 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Transfer learning (TL) is the process by which some aspects of a machine learning model generated on a source task is transferred to a target task, to simplify the learning required to solve the target. TL in Genetic Programming (GP) has not received much attention, since it is normally assumed that an evolved symbolic expression is specifically tailored to a problem’s data and thus cannot be used in other problems. The goal of this work is to present a broad and diverse study of TL in GP, considering a varied set of source and target tasks, and dealing with questions that have received little, or no attention, in previous GP literature. In particular, this work studies the performance of transferred solutions when the source and target tasks are from different domains, and when they do not share a similar input feature space. Additionally, the relationship between the success and failure of transferred solutions is studied, considering different source and target tasks. Finally, the predictability of TL performance is analyzed for the first time in GP literature. GP-based constructive induction of features is used to carry out the study, a wrapper-based approach where GP is used to construct feature transformations and an additional learning algorithm is used to fit the final model. The experimental work presents several notable results and contributions. First, TL is capable of generating solutions that outperform, in many cases, baseline methods in classification and regression tasks. Second, it is shown that some problems are good source problems while others are good targets in a TL system. Third, the transferability of solutions is not necessarily symmetric between two problems. Finally, results show that it is possible to predict the success of TL in some cases, particularly in classification tasks. Keywords Transfer learning · Constructive induction of features · Genetic Programming
* Leonardo Trujillo [email protected] Extended author information available on the last page of the article
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Genetic Programming and Evolvable Machines
1 Introduction One of the main practical limitations for the development of real-world machine learning systems is the availability of useful training and validation data. Given the ubiquity of computer systems and digital sensors this may seem counter intuitive, but good data, from the machine learning perspective, can still be hard to come by in many domains [1]. For this reason, the ability to extend the usefulness of data across multiple domains and tasks can simplify the manner in which machine learning models are generated. One promising way forward is a technique that is widely known as transfer learning (TL), where some aspects of a machine learning model that was generated using
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