# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 2 Related Work
### Related Works  
* **Tri-level Attribute Reduction in Rough Set Theory:**[PERSON] and [PERSON] (2022) introduced a tri-level attribute reduction framework, extending traditional attribute reduction by incorporating object-specific reducts at the micro-bottom level. This approach enhances both classification-specific and class-specific reducts, providing a more granular and hierarchical understanding of attribute reduction in Rough Set Theory [15].
* **Seasonal Air Quality Prediction using Regularized Combinational LSTM:** In a study by [PERSON] et al., 2023, a framework for predicting air quality seasonally using Regularized Combinational LSTM (REG-CLSTM) was proposed. The model aims to improve air quality prediction accuracy and reduce error rates by leveraging a large real-time dataset. The study employed a rough set-wrapper method for significant feature extraction and addressed the challenge of providing seasonal limit ranges for pollutants. The proposed pyramid learning-based hybridized deep learning framework can play a crucial role in warning policymakers to reduce activities that contribute to air pollution [16].
* **Financial Risk Early Warning Model:**[PERSON] and [PERSON] (2024) developed a financial risk early warning model for listed companies using Rough Set Theory and a BP neural network. Their model achieved high accuracy, recall, and F1-scores, demonstrating its effectiveness in predicting financial risks and providing decision support for financial management [17].
* **Boundary-wise Loss using Fuzzy Rough Sets:** In a study by [PERSON] et al., 2024, a novel boundary-wise loss function for medical image segmentation was proposed, leveraging fuzzy rough sets. The loss function, based on the lower approximation of fuzzy rough sets, focuses on improving the delineation of object boundaries in semantic segmentation. Experiments demonstrated that the proposed loss outperforms traditional pixel-wise and region-wise losses in terms of Hausdorff distance and symmetric surface distance, while maintaining competitive performance in Dice coefficient and pixel-wise accuracy. The study highlights the importance of boundary-wise loss in producing more accurate shapes of segmented objects [18].
* **Rough Set Theory in Vector Spaces:**[PERSON] and [PERSON] (2024) explored the application of Rough Set Theory to finite-dimensional vector spaces. They defined an indiscernibility relation and studied partitions, reducts, and dependency measures, providing a theoretical foundation for applying RST in linear algebra contexts [19].
* **Intelligent Recommender System for Disease Prediction:**[PERSON] and [PERSON] (2024) proposed a hybrid recommender system using machine learning association rules and Rough Set Theory for disease prediction from incomplete symptom sets. Their system achieved high accuracy and precision, particularly in detecting neurodevelopmental diseases [20].
* **Student Performance Prediction:**[PERSON] and [PERSON] (2025) developed a hybrid deep learning model combined with entropy-weighted rough set feature mining for predicting student performance. Their approach, which optimizes hyperparameters using a Galactic Rider Swarm Optimization algorithm, achieved high sensitivity and accuracy rates [21].  
To provide a clear overview of the related works, Table 1 summarizes the key studies, their contributions, and the domains in which they were applied.