Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

• URL: http://arxiv.org/abs/2309.04612v2
• Date: Thu, 14 Sep 2023 16:56:50 GMT
• Title: Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing
• Authors: Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, Yanjie Fu
• Abstract summary: We propose a principled and generic representation-crossing framework to solve self-optimizing feature generation. We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method.
• Score: 37.73656271138515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git.

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