Self-Optimizing Feature Transformation

TL;DR

This paper introduces a self-optimizing framework for feature transformation that enhances feature space quality using reinforcement learning, addressing limitations of existing methods and demonstrating superior performance across multiple datasets.

Contribution

The paper presents an improved self-optimizing feature transformation framework with advanced state representation and unbiased policy learning, enabling more effective and automated feature space optimization.

Findings

Outperforms previous methods in outlier detection tasks

Effective across five diverse datasets

Improves feature transformation automation

Abstract

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality, enhance model generalization, overcome data sparsity, and expand the availability of classic models. Current research focuses on domain knowledge-based feature engineering or learning latent representations; nevertheless, these methods are not entirely automated and cannot produce a traceable and optimal representation space. When rebuilding a feature space for a machine learning task, can these limitations be addressed concurrently? In this extension study, we present a self-optimizing framework for feature transformation. To achieve a better performance, we improved the preliminary work by (1) obtaining an advanced state representation for enabling reinforced agents to comprehend the current feature set better; and (2) resolving Q-value overestimation in reinforced agents for learning unbiased and effective policies. Finally, to make experiments more convincing than the preliminary work, we conclude by adding the outlier detection task with five datasets, evaluating various state representation approaches, and comparing different training strategies. Extensive experiments and case studies show that our work is more effective and superior.