One button machine for automating feature engineering in relational databases

By Hoang Thanh Lam, Johann-Michael Thiebaut, Mathieu Sinn, Tiep Mai, O. Alkan. 2017

105 citations recorded by the source corpus.

Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16% to 24% data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “More recently, greedy search (Dor & Reich, 2012; Khurana et al., 2016) and depth-first search (Lam et al., 2017) in combination with feature selection have been used to create a sequence of operators.”
Survey on Automated Machine Learning (2019, background): “This process is can be interpreted as a transformation tree T : the root node represents the original features; each edge applies one specific operator leading to a transformed feature set (Khurana et al., 2016; Lam et al., 2017).”
Techniques for Automated Machine Learning (2019, methodology): “Edges are SIGKDD Explorations Volume 22, Issue 2 37 Category Technique Bayesian Optimization Reinforcement Learning Evolutionary Algorithm Gradient Approach Otherss Automated Feature Engineering (AutoFE) - FeatureRL [49] NFS [14] Genetic Programming Feature Engineering [84] Genetic Programming Skewe”
Feature Engineering for Predictive Modeling using Reinforcement Learning (2017, methodology): “A similar approach is adopted by One Button Machine (Lam et al. 2017).”
Human-AI Collaboration in Data Science (2019, methodology): “Feature engineering is often considered to be the most time consuming step of the data science process [33], and techniques such as Deep Feature Synthesis [35] and One Button Machine [43] automate the task of generating ML-ready features from multi-relational databases.”