Data-centric AI focuses on understanding and utilizing high-quality, relevant
data in training machine learning (ML) models, thereby increasing the
likelihood of producing accurate and useful results. Automatic feature
augmentation, aiming to augment the initial base table with useful features
from other tables, is critical in data preparation as it improves model
performance, robustness, and generalizability. While recent works have
investigated automatic feature augmentation, most of them have limited
capabilities in utilizing all useful features as many of them are in candidate
tables not directly joinable with the base table. Worse yet, with numerous join
paths leading to these distant features, existing solutions fail to fully
exploit them within a reasonable compute budget. We present FeatNavigator, an
effective and efficient framework that explores and integrates high-quality
features in relational tables for ML models. FeatNavigator evaluates a feature
from two aspects: (1) the intrinsic value of a feature towards an ML task
(i.e., feature importance) and (2) the efficacy of a join path connecting the
feature to the base table (i.e., integration quality). FeatNavigator
strategically selects a small set of available features and their corresponding
join paths to train a feature importance estimation model and an integration
quality prediction model. Furthermore, FeatNavigator's search algorithm
exploits both estimated feature importance and integration quality to identify
the optimized feature augmentation plan. Our experimental results show that
FeatNavigator outperforms state-of-the-art solutions on five public datasets by
up to 40.1% in ML model performance.

数据中心的人工智能通过探索和整合关系表中的高质量特征用于机器学习模型，提高模型性能，并在五个公共数据集上优于最先进的解决方案达到 40.1% 的提升。