Summary of Beyond Tree Models: a Hybrid Model Of Kan and Gmlp For Large-scale Financial Tabular Data, by Mingming Zhang et al.
Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data
by Mingming Zhang, Jiahao Hu, Pengfei Shi, Ningtao Wang, Ruizhe Gao, Guandong Sun, Feng Zhao, Yulin kang, Xing Fu, Weiqiang Wang, Junbo Zhao
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This hybrid neural network, called TKGMLP, is designed to handle large-scale tabular data, which is crucial in financial scenarios. The model combines shallow Kolmogorov Arnold Networks with Gated Multilayer Perceptron to improve performance and scalability. It outperforms traditional tree-based models and current benchmarks on a real-world credit scoring dataset. Additionally, the researchers propose a novel feature encoding method for numerical data, which significantly improves prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use computers to analyze big tables of numbers that are important in finance. The old way was using tree-based models, but they had problems with really big datasets. So, the scientists created a new model called TKGMLP that combines two other types of neural networks. This new model does better than the old ones and can even get better as the dataset gets bigger. It also helps solve a problem in finance where there are lots of numbers in the data. |
Keywords
» Artificial intelligence » Neural network
