Summary of Lightgbm Robust Optimization Algorithm Based on Topological Data Analysis, by Han Yang et al.
LightGBM robust optimization algorithm based on topological data analysis
by Han Yang, Guangjun Qin, Ziyuan Liu, Yongqing Hu, Qinglong Dai
First submitted to arxiv on: 19 Jun 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 The proposed TDA-LightGBM algorithm enhances the robustness of the Light Gradient Boosting Machine (LightGBM) for image classification. It does this by partitioning feature engineering into pixel and topological streams, combining features to create a comprehensive input for LightGBM. This approach incorporates traditional feature engineering methods with topological structure information to better capture intrinsic image features. The goal is to overcome challenges related to unstable feature extraction and decreased accuracy due to data noise in conventional image processing. Experimental results show that TDA-LightGBM improves accuracy by 3% on the SOCOFing dataset across five classification tasks under noisy conditions, and by 0.5% on two classification tasks in noise-free scenarios. Additionally, it increases accuracy for the Ultrasound Breast Images and Masked CASIA WebFace datasets by 6% and 15%, respectively, outperforming LightGBM with noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed TDA-LightGBM algorithm helps improve image classification by reducing the impact of noisy data on Light Gradient Boosting Machine. It does this by combining traditional feature engineering with topological information to better understand images. This makes it more accurate and reliable for tasks like classifying breast cancer and recognizing faces. Tests showed that this approach works well, even when there’s noise in the data. |
Keywords
» Artificial intelligence » Boosting » Classification » Feature engineering » Feature extraction » Image classification
