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.

通过引入一种交互感知的增强生成视角，我们重新定义特征空间重建为通过选择创建有意义的特征和控制特征集大小的嵌套过程，从而自动化特征和操作的选择以及特征交叉。通过结合统计度量，我们基于所选特征之间的相互作用强度奖励智能体，实现了对特征空间的智能和高效的探索，从而模拟人体决策过程。通过进行大量实验证实了我们提出的方法。

通过感知交互意识的自动转换提升特征认知能力

Feature Cognition Enhancement via Interaction-Aware Automated  Transformation

Automated Feature Engineering (AutoFE) has become an important task for any
machine learning project, as it can help improve model performance and gain
more information for statistical analysis. However, most current approaches for
AutoFE rely on manual feature creation or use methods that can generate a large
number of features, which can be computationally intensive and lead to
overfitting. To address these challenges, we propose a novel convolutional
method called FeatGeNN that extracts and creates new features using correlation
as a pooling function. Unlike traditional pooling functions like max-pooling,
correlation-based pooling considers the linear relationship between the
features in the data matrix, making it more suitable for tabular data. We
evaluate our method on various benchmark datasets and demonstrate that FeatGeNN
outperforms existing AutoFE approaches regarding model performance. Our results
suggest that correlation-based pooling can be a promising alternative to
max-pooling for AutoFE in tabular data applications.

自动特征工程（AutoFE）是任何机器学习项目的重要任务，可以帮助改善模型性能并为统计分析提供更多信息。为了解决当前 AutoFE 方法中存在的手动特征创建和特征数量过多问题，我们提出了一种基于相关性的卷积方法 ——FeatGeNN。我们在各种基准数据集上评估了我们的方法，并证明 FeatGeNN 在模型性能方面优于现有的 AutoFE 方法。我们的结果表明，在表格数据应用中，基于相关性的池化可以成为 AutoFE 中替代 max-pooling 的有前景的选择。

FeatGeNN：基于相关性特征提取的表格数据模型性能改进

FeatGeNN: Improving Model Performance for Tabular Data with  Correlation-based Feature Extraction

We introduce the concept of programmable feature engineering for time series
modeling and propose a feature programming framework. This framework generates
large amounts of predictive features for noisy multivariate time series while
allowing users to incorporate their inductive bias with minimal effort. The key
motivation of our framework is to view any multivariate time series as a
cumulative sum of fine-grained trajectory increments, with each increment
governed by a novel spin-gas dynamical Ising model. This fine-grained
perspective motivates the development of a parsimonious set of operators that
summarize multivariate time series in an abstract fashion, serving as the
foundation for large-scale automated feature engineering. Numerically, we
validate the efficacy of our method on several synthetic and real-world noisy
time series datasets.

提出了可编程特征工程的概念，为时间序列建模提供一种以自旋气体动力伊辛模型为基础的特征程序框架，从而实现大规模自动化特征工程。