Selection of Secondary Features from Multi-Table Data for Classification

Multi-table data is common in organizations, and its analysis is crucial for applications such as fraud detection, service improvement, and customer relationship management. Processing this type of data requires flattening, which transforms the multi-table structure into a single flat table by creating aggregates from the original variables. Several propositionalization tools aim to automate this process, but as data complexity increases due to the number of tables and relationships, the effectiveness of flattening decreases. To enhance the quality of propositionalization, it is essential to develop automated preprocessing systems that optimize the construction of aggregates by focusing on the most informative variables.The objective of this article is to propose a method for selecting secondary variables and to demonstrate that this approach effectively filters out non-informative variables using a univariate analysis. Finally, we will show, using a set of academic datasets, that reducing the number of secondary variables to only those that are truly informative can improve classification performance.