Recent advancements in feature representation and dimension reduction have highlighted their crucial role in enhancing the efficacy of predictive modeling. This work introduces TemporalPaD, a novel end-to-end deep learning framework designed for temporal pattern datasets. TemporalPaD integrates reinforcement learning (RL) with neural networks to achieve concurrent feature representation and feature reduction. The framework consists of three cooperative modules: a Policy Module, a Representation Module, and a Classification Module, structured based on the Actor-Critic (AC) framework. The Policy Module, responsible for dimensionality reduction through RL, functions as the actor, while the Representation Module for feature extraction and the Classification Module collectively serve as the critic. We comprehensively evaluate TemporalPaD using 29 UCI datasets, a well-known benchmark for validating feature reduction algorithms, through 10 independent tests and 10-fold cross-validation. Additionally, given that TemporalPaD is specifically designed for time series data, we apply it to a real-world DNA classification problem involving enhancer category and enhancer strength. The results demonstrate that TemporalPaD is an efficient and effective framework for achieving feature reduction, applicable to both structured data and sequence datasets. The source code of the proposed TemporalPaD is freely available as supplementary material to this article and at http://www.healthinformaticslab.org/supp/.

翻译：特征表示与降维技术的最新进展凸显了其在提升预测建模效能中的关键作用。本文提出了TemporalPaD，一种专为时序模式数据集设计的新型端到端深度学习框架。TemporalPaD将强化学习与神经网络相结合，以实现并行的特征表示与特征降维。该框架基于Actor-Critic架构构建，包含三个协同模块：策略模块、表示模块与分类模块。其中，负责通过强化学习进行降维的策略模块充当actor，而负责特征提取的表示模块与分类模块共同作为critic。我们采用29个UCI数据集（验证特征降维算法的知名基准），通过10次独立测试与10折交叉验证对TemporalPaD进行了全面评估。此外，鉴于TemporalPaD专为时序数据设计，我们将其应用于涉及增强子类别与增强子强度的真实世界DNA分类问题。结果表明，TemporalPaD是一种高效且有效的特征降维框架，可同时适用于结构化数据与序列数据集。所提TemporalPaD的源代码已作为本文补充材料公开，亦可从http://www.healthinformaticslab.org/supp/获取。