@article{Khurana_Samulowitz_Turaga_2018, title={Feature Engineering for Predictive Modeling Using Reinforcement Learning}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/11678}, DOI={10.1609/aaai.v32i1.11678}, abstractNote={ <p> Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly captures the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Khurana, Udayan and Samulowitz, Horst and Turaga, Deepak}, year={2018}, month={Apr.} }