Título: Dynamic Feature Acquisition and Classification with Transformers in a Reinforcement Learning Framework
Autores: Gabriel Baruque, Wouter Caarls
Resumo: Classification with Costly Features (CwCF) addresses the challenge of balancing feature acquisition costs with classification accuracy. Traditional methods often rely on static feature subsets, leading to suboptimal resource utilization. This paper proposes a novel reinforcement learning (RL) framework enhanced with Transformer architecture to dynamically select features in a sequential manner, optimizing cost-accuracy trade-offs. Our model leverages the Transformer architecture to handle variable-length input sequences, combining embeddings for both feature values and their respective feature identities, while integrating Deep Q-Networks for decision-making. Experiments across synthetic and real-world benchmarks demonstrate that the Transformer-based approach achieves competitive accuracy compared to prior RL methods, though it falls short of state-of-the-art (SotA) models in cumulative return. The results highlight the model’s ability to adaptively gather informative features, emphasizing accuracy over cost minimization. This work brings out the potential of sequence-based architectures in dynamic feature selection and opens discussion for further exploration of Transformer-RL hybrids in cost-sensitive classification tasks.
Palavras-chave: Costly Features; Dynamic Feature Acquisition; Transformers; Classification; Reinforcement Learning.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1146000
Artigo em PDF: CBIC_2025_paper1146000.pdf
Arquivo BibTeX:
CBIC_2025_1146000.bib