Tabular data is the most commonly used form of data in industry according to a Kaggle ML
and DS Survey. Gradient Boosting Trees, Support Vector Machine, Random Forest, and …

For many machine-learning tasks, augmenting the data table at hand with features built from
external sources is key to improving performance. For instance, estimating housing prices …

Feature engineering often plays a crucial role in building mining systems for tabular data,
which traditionally requires experienced human experts to perform. Thanks to the rapid …

Feature construction can contribute to comprehensibility and performance of machine
learning models. Unfortunately, it usually requires exhaustive search in the attribute space …

The majority of data scientists and machine learning practitioners use relational data in their
work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learning …

Feature engineering is a crucial but tedious task that requires up to 80% of the total time in
data science projects. A significant challenge is when data consists of tables from different …

Relational databases are extensively utilized in a variety of modern information system
applications, and they always carry valuable data patterns. There are a huge number of data …

Predictive modeling based on machine learning with medical data has great potential to
improve healthcare and reduce costs. However, two hurdles, among others, impede its …

Automated feature engineering has gained considerable attention in academia and industry.
Nevertheless, existing systems often lack practical scalability and efficiency. This paper …

We consider the feature-generation task wherein we are given a database with entities
labeled as positive and negative examples, and the goal is to find feature queries that allow …