arXiv AI
Jul 3
Evolutionary Feature Engineering for Structured Data
arXiv:2607. 01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization.
Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification
arXiv:2608. 14209v1 Announce Type: new Abstract: Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple base learner.
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Hugging Face Trending Papers
Jul 2
Evolutionary Feature Engineering for Structured Data
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data.
arXiv AI
Jun 9
FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
arXiv:2606. 07704v1 Announce Type: cross Abstract: Symbolic regression aims to uncover explicit scientific laws from data.
arXiv AI
Jun 9
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
arXiv:2606. 08800v1 Announce Type: new Abstract: In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must leverage the expert documentation governing these domains.
arXiv AI
Jul 21
Feature Generation Using LLMs: An Evolutionary Algorithm Approach
arXiv:2607. 16255v1 Announce Type: cross Abstract: A crucial step in machine learning pipelines is to present each entity with features or attributes that are representative of the characteristics of the processed entities.
arXiv Machine Learning
Jul 28
An Empirical Study of Feature Selection Granularity
arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
arXiv Machine Learning
Jul 21
MinShap: A Shapley-Based Framework for Feature Redundancy
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
arXiv AI
Jul 28
TopoFE: topology-aware LLM-guided Automated Feature Engineering
arXiv:2607. 23286v1 Announce Type: new Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space.
arXiv Machine Learning
Aug 4
Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection
arXiv:2511. 20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores.
arXiv Machine Learning
Jun 9
Decomposable Neuro Symbolic Regression
arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.
arXiv Machine Learning
Jun 4
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
arXiv:2606. 04360v1 Announce Type: cross Abstract: Symbolic regression (SR) discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on scalar feedback such as MSE.
arXiv Machine Learning
Jul 28
Variable Importance Identification Through Lazy Training for Binary Classification
arXiv:2607. 22979v1 Announce Type: cross Abstract: Deep neural networks have been widely used in many applications (e.