OpenFE++: Efficient Automated Feature Generation via Feature Interaction
Lei Wang, Yu Shi, Y. J. Jin, Jian Li · Society for Industrial and Applied Mathematics eBooks · 2025
Automated feature generation can greatly enhance the performance of machine learning models in many tabular and time-series prediction problems. The current state-of-the-art method, OpenFE, follows the “expand-and-reduce” framework, which generates a candidate feature set and subsequently extracts the most effective features from this set. Nevertheless, with the increase in the number of features and the length of time series, feature generation algorithms based on expand-and-reduce often produce an overwhelmingly large pool of candidate features, rendering the identification of effective features quite time-consuming and more prone to overfitting. To resolve this issue, we propose OpenFE++, which leverages the feature interactions from both the feature and temporal dimensions to construct a substantially reduced candidate feature set, thereby enhancing efficiency and effectiveness in feature generation. In the feature dimension, OpenFE++ utilizes locally interacted features to generate meaningful candidate features without exhaustively enumerating all possibilities. In the temporal dimension, it evaluates the lagged effects among different features and generates temporally meaningful features via the representative lagged periods, eliminating the need for enumeration in sequence length. Thus, OpenFE++ can efficiently generate effective, generalizable and interpretable features to boost the forecasting performance of machine learning models. We conduct extensive experiments on fourteen widely used benchmark datasets (ten benchmarks for tabular tasks and four benchmarks for time-series tasks) to demonstrate that OpenFE++ outperforms other baseline models in both efficiency and effectiveness.