TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start

This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The model aims to provide accurate credit assessment prediction for new supply chain borrowers with limited historical data. Here, the two-stream domain adaptation architecture with correlation alignment (CORAL) loss is used as a core model and is equipped with transformer, which provides insights about the learned features and allow efficient parallelization during training. Thanks to the domain adaptation capability of the proposed model, the domain shift between the source and target domain is minimized. Therefore, the model exhibits good generalization where the source and target do not follow the same distribution, and a limited amount of target labeled instances exist. Furthermore, we employ Local Interpretable Model-agnostic Explanations (LIME) to provide more insight into the model prediction and identify the key features contributing to supply chain credit assessment decisions. The proposed model addresses four significant supply chain credit assessment challenges: domain shift, cold start, imbalanced-class and interpretability. Experimental results on a real-world data set demonstrate the superiority of TransCORALNet over a number of state-of-the-art baselines in terms of accuracy. The code is available on GitHub https://github.com/JieJieNiu/TransCORALN .Comment: 13 pages, 7 figure

Designing Active Objects in Degas

and their applications. SMC is sponsored by the Netherlands Organization for Scientific Research (NWO). CWI is a member o

Information and Computing Sciences

right of this thesis. Any person(s) intending to use a part or the whole of the materials in this thesis in a proposed publication must seek copyright release from the Head of the department of ICS. Summary Genetic algorithms are simplified and abstract computational models inspired by natural selection. They have been widely applied to optimization and search problems, but the use of a fixed and pre-defined fitness function is usually bi-ased and difficult to define for complicated problems. Coevolution uses multi-ple populations to evaluate against each others and offers an approach to form adaptive criteria for evaluating individuals. A crucial question is how to define efficient evaluation methods in coevolutionary algorithms. In this research, we compare five different evaluation methods in coevolution on three test-based problems. The sizes of the problems are selected such that an evaluation against all possible test cases is feasible. Two measures are used for the comparisons, i.e., the objective fitness derived from evaluating solution