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README.md

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----
-license: apache-2.0
----
+---
+license: mit
+task_categories:
+- tabular-regression
+- robotics
+tags:
+- regression
+- tabular-data
+- imbalanced-learning
+- sep-forecasting
+- solar-physics
+- machine-learning
+- deep-learning
+---
+
+# Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
+
+This repository contains the datasets used in the paper "[Highly Imbalanced Regression with Tabular Data in SEP and Other Applications](https://huggingface.co/papers/2509.16339)". This work investigates highly imbalanced regression with tabular data, where imbalance ratios exceed 1,000. It's particularly relevant for applications like forecasting the intensity of rare harmful Solar Energetic Particle (SEP) events.
+
+The associated code and method implementation can be found on GitHub: [https://github.com/Machine-Earning/CISIR](https://github.com/Machine-Earning/CISIR)
+
+## Overview
+
+This repository contains the official implementation of **CISIR** (Correlation, Involution, Stratified Importance Regression), a novel method for highly imbalanced regression with tabular data. The method is particularly applicable to SEP (Solar Energetic Particle) forecasting research for NASA and other domains requiring accurate prediction of rare, high-impact events.
+
+### Key Contributions
+
+-   **Correlation-aware loss function** that considers the correlation between predicted and actual values
+-   **Monotonically Decreasing Involution (MDI) importance** weighting that outperforms traditional convex importance functions
+-   **Stratified sampling strategy** that ensures rare instances are included in mini-batches
+-   **Comprehensive evaluation** on five highly imbalanced datasets with imbalance ratios > 1,000
+
+## Datasets
+
+We evaluate our method on five highly imbalanced datasets. All datasets exhibit high imbalance ratios (ρ > 1,000), making them ideal for evaluating highly imbalanced regression methods.
+
+### SEP Datasets
+-   **SEP-EC**: Forecasts the change (delta) in proton intensity based on features from electron intensity and CMEs (Coronal Mass Ejections)
+-   **SEP-C**: Forecasts peak proton intensity based on CME characteristics
+
+### Other Datasets
+-   **SARCOS**: Estimates the torque vector based on joint-state inputs for a 7-DOF robot arm
+-   **Blog Feedback (BF)**: Forecasts the number of comments based on textual, temporal, and engagement features  
+-   **Online News Popularity (ONP)**: Estimates the number of shares of an article based on content, topic, and sentiment attributes
+
+### Data Availability
+The datasets are available for direct download at: https://huggingface.co/datasets/Machine-Earning/CISIR-datasets/resolve/main/CISIR-data.zip
+
+## Citation
+
+If you find this repository useful in your research, please consider giving a star ⭐ and a citation:
+
+```bibtex
+@inproceedings{moukpe2024cisir,
+  title={Highly Imbalanced Regression with Tabular Data in SEP and Other Applications},
+  author={Moukpe, Josias K. and Chan, Philip K. and Zhang, Ming},
+  booktitle={Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA)},
+  year={2024},
+  organization={IEEE}
+}
+```