![]() In addition to the unifying survey, the main novelty of this paper is a unifying methodology that combines propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. This paper contributes an improved understanding of these data transformation techniques by presenting a unified terminology and definitions, by explaining the similarities and differences of the two definitions as variants of a unified complex data transformation task, by exploring the apparent differences between the two approaches, and by outlining some of their advantages and disadvantages. While both approaches aim at transforming data into a tabular data format, the approaches use different terminology and task definitions, claim to have different goals, and are used in very different contexts. The first aim of this paper is to present a unifying survey of propositionalization and embedding data transformation approaches. In the relational learning context of this paper, both approaches take as input a relational data set (e.g., a given relational database) and transform it into a single data table format, which is then used as an input to a propositional learning algorithm of choice. 2018) have only recently been recognized by RL and ILP researchers as a powerful technique for preprocessing relational and complex structured data. 2001 Železný and Lavrač 2006) is a well known data transformation technique used in relational learning (RL) and inductive logic programming (ILP) (Muggleton 1992 Lavrač and Džeroski 1994 De Raedt 2008), embeddings (Mikolov et al. While propositionalization (Kramer et al. Two of the most prominent data transformation approaches outlined in this paper are propositionalization and embeddings. ![]() The key element of the success of modern data transformation methods is that similarities of original instances and their relations are encoded as distances in the target vector space. Most of the modern data processing techniques enable data fusion from different data types and formats into a single table data representation, which is expected by standard machine learning techniques including rule learning, decision tree learning, support vector machines (SVMs), deep neural networks (DNNs), etc. ![]() The results show that the new algorithms can outperform existing relational learners and can solve much larger problems.ĭata preprocessing for machine learning is a great challenge for a data scientist faced with large quantities of data in different forms and sizes. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. ![]() While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources.
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