Mutually Promoted or Mutually Restricted? A Multi-level Co-adaptation Regularized Deep Learning Method for Multimodal Data Based Default Risk Prediction
2020
- 167Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage167
- Abstract Views131
- Downloads36
Article Description
The proliferation of social media along with AI techniques have flourished the use of multimodal data to default risk prediction. In this study, we submit a pair of opposite effects for multimodal data based default risk prediction, namely, mutual promotion effects and mutual restriction effetcs. Given such a premise, we firstly extract hard features, social media textual features, and facial attractiveness features for each of the borrowers, and then we propose a novel multi-level co-adaptation regularized multimodal deep learning method. Our proposed method is capable of identifying mutually promoted patterns among multimodal features, besides, our method opens up a novel avenue for balancing the trade-off between feature-wise co-adaptation and modality-wise co-adaptation. Moreover, we theoretically prove that by virtue of our method, multimodal features with multi-level co-adaptations can be automatically detected. The experimental resuts show that our method beats existing methods by a significant margin.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know