Cognito: Automated Feature Engineering for Supervised Learning
Feature engineering involves constructing novel features from given data with the goal of improving predictive learning performance. Feature engineering is predominantly a human intensive and time consuming step that is central to the data science workflow. In this paper, we present a novel system called "Cognito" that performs automatic feature engineering on a given dataset for supervised learning. The system explores various feature construction choices in a hierarchical and non exhaustive manner, while progressively maximizing the accuracy of the model through a greedy exploration strategy. Additionally, the system allows users to specify domain or data specific choices to prioritize the exploration. Cognito is capable of handling large datasets through sampling and built in parallelism, and integrates well with a state of the art model selection strategy. We present the design and operation of Cognito, along with experimental results on eight real datasets to demonstrate its efficacy.
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)