TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917788429320192 |
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| author | Mu, Xuechen Huang, Zhenyu Li, Kewei Zhang, Haotian Wang, Xiuli Fan, Yusi Zhang, Kai Zhou, Fengfeng |
| author_facet | Mu, Xuechen Huang, Zhenyu Li, Kewei Zhang, Haotian Wang, Xiuli Fan, Yusi Zhang, Kai Zhou, Fengfeng |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction Mu, Xuechen Huang, Zhenyu Li, Kewei Zhang, Haotian Wang, Xiuli Fan, Yusi Zhang, Kai Zhou, Fengfeng Machine Learning Artificial Intelligence Genomics 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/. |
| title | TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction |
| topic | Machine Learning Artificial Intelligence Genomics |
| url | https://arxiv.org/abs/2409.18597 |