Neuro-Symbolic Embedding for Efficient and Effective Feature Selection via Autoregressive Generation

Core Concepts

A novel neuro-symbolic framework that efficiently identifies short and effective feature subsets by preserving feature selection knowledge in a continuous embedding space and optimizing for both downstream performance and feature redundancy.

Abstract

The content presents a novel feature selection framework called FSNS (Feature Selection via Neuro-Symbolic Embedding) that aims to efficiently identify effective feature subsets. The key insights are: Feature selection is reformulated as a neuro-symbolic generative learning task, where feature ID tokens are treated as symbols to capture the intricate correlations among features. An encoder-decoder-evaluator framework is developed to preserve the intelligence of feature selection into a continuous embedding space. This allows converting the discrete feature selection process into a continuous optimization problem. Within the learned embedding space, a multi-gradient search algorithm is leveraged to find robust and generalized embeddings that optimize for both downstream model performance and feature subset redundancy. The final feature subset is reconstructed from the optimized embedding using an autoregressive decoding process. Comprehensive experiments on 16 real-world datasets demonstrate the effectiveness of the proposed FSNS framework, outperforming various baseline feature selection methods. The framework is shown to be robust to different downstream models, able to handle both supervised and unsupervised settings, and efficient in terms of time and space complexity.

Stats

The proposed FSNS framework can improve downstream task accuracy by 3% on average compared to the best baseline method. FSNS can reduce feature subset redundancy by up to 100% while maintaining high downstream performance. The unsupervised variant of FSNS can achieve similar performance to the supervised version, while saving significant time on data collection.

Quotes

"Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resources, accelerate the model learning process, and improve the model overall performance." "To bridge these gaps, we reformulate feature selection through a neuro-symbolic lens and introduce a novel generative framework aimed at identifying short and effective feature subsets." "We preserve the intelligence of feature selection into a continuous embedding space for efficient search. Within the learned embedding space, we leverage a multi-gradient search algorithm to find more robust and generalized embeddings with the objective of improving model performance and reducing feature subset redundancy."

Key Insights Distilled From

Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation

by Nanxu Gong,W... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17157.pdf

Deeper Inquiries

How can the proposed neuro-symbolic framework be extended to handle dynamic feature spaces, where new features are continuously added over time

The proposed neuro-symbolic framework can be extended to handle dynamic feature spaces by incorporating mechanisms for adaptive learning and feature selection. One approach could involve implementing a continuous learning process where the model continuously updates the feature subset embedding space as new features are introduced. This can be achieved by integrating online learning techniques that can adapt to changes in the feature space over time. Additionally, the framework can incorporate mechanisms for feature importance reevaluation and feature subset reconstruction to accommodate the dynamic nature of the feature space. By continuously updating the feature subset embedding space based on the evolving feature set, the model can effectively handle dynamic feature spaces and maintain optimal feature selection performance.

What are the potential applications of the learned feature subset embedding space beyond the task of feature selection, such as in transfer learning or meta-learning

The learned feature subset embedding space has various potential applications beyond feature selection, particularly in transfer learning and meta-learning tasks. In transfer learning, the feature subset embedding space can serve as a knowledge representation that captures the relationships and interactions between features in a dataset. This learned knowledge can be transferred to new tasks or domains, enabling more efficient model adaptation and improved generalization. In meta-learning, the feature subset embedding space can be leveraged to facilitate the rapid learning of new tasks by providing a structured representation of feature relationships and importance. By utilizing the learned embedding space in transfer learning and meta-learning scenarios, models can benefit from enhanced feature understanding and improved performance across diverse tasks and datasets.

Can the neuro-symbolic approach be applied to other discrete optimization problems in machine learning, such as neural architecture search or hyperparameter tuning

The neuro-symbolic approach can indeed be applied to other discrete optimization problems in machine learning, such as neural architecture search (NAS) and hyperparameter tuning. In NAS, the framework can be adapted to learn and optimize the architecture of neural networks by treating architectural components as symbolic tokens and utilizing a generative framework to explore and evaluate different network configurations. By embedding neural architecture components into a continuous space, the model can efficiently search for optimal network architectures based on performance and complexity criteria. Similarly, in hyperparameter tuning, the neuro-symbolic approach can be used to learn the relationships between hyperparameters and model performance, enabling automated tuning and optimization of hyperparameter settings for improved model performance. By extending the neuro-symbolic framework to these optimization problems, models can benefit from enhanced efficiency, automation, and performance optimization in various machine learning tasks.