Feature Interaction Aware Automated Data Representation Transformation

TL;DR

This paper introduces an interaction-aware reinforcement learning approach for automated feature engineering, focusing on creating meaningful, explainable feature spaces that improve model performance and convergence.

Contribution

It proposes a hierarchical reinforcement learning framework that mimics human decision-making to systematically generate and select features, addressing limitations of existing AutoFE methods.

Findings

Improved feature space quality through interaction-based rewards.

Faster convergence to optimal features compared to traditional AutoFE.

Enhanced model performance with more interpretable features.

Abstract

Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively. Recent advancements in automated feature engineering (AutoFE) have made significant progress in addressing various challenges associated with representation learning, issues such as heavy reliance on intensive labor and empirical experiences, lack of explainable explicitness, and inflexible feature space reconstruction embedded into downstream tasks. However, these approaches are constrained by: 1) generation of potentially unintelligible and illogical reconstructed feature spaces, stemming from the neglect of expert-level cognitive processes; 2) lack of systematic exploration, which subsequently results in slower model convergence for identification of optimal feature space. To address these, we introduce an interaction-aware reinforced generation perspective. We redefine feature space reconstruction as a nested process of creating meaningful features and controlling feature set size through selection. We develop a hierarchical reinforcement learning structure with cascading Markov Decision Processes to automate feature and operation selection, as well as feature crossing. By incorporating statistical measures, we reward agents based on the interaction strength between selected features, resulting in intelligent and efficient exploration of the feature space that emulates human decision-making. Extensive experiments are conducted to validate our proposed approach.