Zoom: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5T…
In recent years, data mining has achieved great success in enormous scenarios. As the foundation of data mining, feature engineering plays an essential role in comprehending and perceiving data. Successful feature engineering can remove irrelevant features, generate informative features, improve model performance, enhance generalization, and provide better interpretation and explanation. However, not all researchers and practitioners are experts in feature engineering, making the automation of feature engineering an indispensable demand. In this talk, I will first introduce what feature engineering is and why it is difficult to automate the feature engineering process. Then, I will focus on (1) automated feature selection (2) automated feature generation, and discuss how the framework of reinforcement learning can be adapted to solve these problems correspondingly. Finally, I will conclude the talk and present the big picture of developing intelligent, interpretable, and interactive automated data science systems.
Kunpeng Liu is an assistant professor from the Department of Computer Science, Portland State University. His research interests are data mining and machine learning, especially in automated data science systems, with application to big data problems, including smart city, privacy-preserving machine learning, reasoning on recommender systems, and user behavior analysis. He is also interested in efficient RLHF and LLM inference.