mlsanityData to 14 Oct 2026

TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction

arXiv 2409.18597 ↗First posted 27 Sep 2024cs.LG cs.AI q-bio.GNcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 45 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

3 of 45 references.

References · 3

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2016An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learningcited by 29

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