  Feature preprocessing, which transforms raw input features into numerical
representations, is a crucial step in automated machine learning (AutoML)
systems. However, the existing systems often have a very small search space for
feature preprocessing with the same preprocessing pipeline applied to all the
numerical features. This may result in sub-optimal performance since different
datasets often have various feature characteristics, and features within a
dataset may also have their own preprocessing preferences. To bridge this gap,
we explore personalized preprocessing pipeline search, where the search
algorithm is allowed to adopt a different preprocessing pipeline for each
feature. This is a challenging task because the search space grows
exponentially with more features. To tackle this challenge, we propose
ClusterP3S, a novel framework for Personalized Preprocessing Pipeline Search
via Clustering. The key idea is to learn feature clusters such that the search
space can be significantly reduced by using the same preprocessing pipeline for
the features within a cluster. To this end, we propose a hierarchical search
strategy to jointly learn the clusters and search for the optimal pipelines,
where the upper-level search optimizes the feature clustering to enable better
pipelines built upon the clusters, and the lower-level search optimizes the
pipeline given a specific cluster assignment. We instantiate this idea with a
deep clustering network that is trained with reinforcement learning at the
upper level, and random search at the lower level. Experiments on benchmark
classification datasets demonstrate the effectiveness of enabling feature-wise
preprocessing pipeline search.
