Heterogeneous Transfer Learning Optimization for Greenhouse Gas Emissions Prediction Using Quantum Annealing

EasyChair Preprint 15004

Abstract

Despite its urgency, the prediction of greenhouse gas (GHG) emissions at the city level is hampered by limited quality and quantity of training data so most predictions of GHG emissions are carried out at the country level, using different feature spaces. Heterogeneous Transfer Learning (HeTL) is considered capable of getting around this limitation due to its ability to facilitate the transfer of knowledge between domains that have different feature spaces and distributions. However, the implementation of HeTL is still haunted by the potential of negative transfer in the knowledge transfer process. Current studies on mitigating negative transfer in HeTL still rely heavily on classical optimization techniques and focus solely on either feature-level or instance-level optimization. In this paper, a method is proposed to optimize the knowledge transfer process in HeTL using quantum annealing. The proposed optimization is carried out in three stages: (1) feature alignment, (2) common feature optimization, and (3) data instance optimization. The proposed method seeks to optimize the knowledge transfer process at both the feature and instance levels. It utilizes a combination of classical computing and quantum computing, thereby combining the advantages of the classical approach and the quantum approach to obtain optimal results.

Keyphrases: Greenhouse gas emissions, Heterogeneous Transfer Learning, Quantum Annealing

@booklet{EasyChair:15004,
  author    = {Wahyu Hidayat and Nur Ulfa Maulidevi and Kridanto Surendro},
  title     = {Heterogeneous Transfer Learning Optimization for Greenhouse Gas Emissions Prediction Using Quantum Annealing},
  howpublished = {EasyChair Preprint 15004},
  year      = {EasyChair, 2024}}