# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 2 Related Work
### Novelty of Our Work  
While prior studies have advanced Rough Set Theory (RST) across domains, our work presents a **unified RST-based feature selection framework**--MLReduct, MLSpecialReduct, and MLFuzzyRoughSet--to optimize machine learning under uncertainty. Unlike traditional methods like PCA, RFE, and our adapted MLVarianceThreshold, which struggle with complex, noisy data due to statistical oversimplification, our RST techniques overcome these limitations. Below, we explicitly outline the **novel contributions** of our proposed methods and how they differ from existing RST-based feature selection techniques:  
* **Novelty**: MLRduct introduces a **systematic approach*
* to identifying minimal attribute subsets (reducts) that preserve the positive region of the full attribute set. Unlike traditional reduct algorithms, MLRduct employs an **exhaustive search*
* combined with a **positive region preservation criterion**, ensuring that the selected features maintain the classification power of the original dataset. * **Advantage over Existing Methods**: While existing reduct algorithms often rely on heuristic or greedy search strategies, MLRduct ensures **optimality*
* by evaluating all possible attribute combinations. This makes it particularly effective for datasets where **attribute dependencies*
* are complex and non-linear. * **Novelty**: MLSpecialReduct is a **dynamic, dependency-driven*
* feature selection method that iteratively builds a reduct by maximizing the dependency between the selected attributes and the decision attribute. It stops when the dependency matches that of the full attribute set or no further improvement is possible. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Contribution** & **Key Findings** \\ \hline [PERSON] and [PERSON] (2022) [15] & Tri-level attribute reduction & Introduced object-specific reducts for granular attribute reduction in data analysis. \\ \hline [PERSON] et al. (2024) [16] & Seasonal air quality prediction using REG-CLSTM & Proposed a hybridized deep learning framework for seasonal air quality prediction, leveraging rough set-wrapper methods for feature extraction and identifying seasonal pollutant trends. \\ \hline [PERSON] and [PERSON] (2024) [17] & Financial risk early warning & Achieved high accuracy in financial risk prediction using RST and BP neural networks. \\ \hline [PERSON] et al. (2024) [18] & Boundary-wise loss using fuzzy rough sets & Introduced a boundary-wise loss function for medical image segmentation, improving boundary delineation and outperforming traditional losses in Hausdorff distance and symmetric surface distance. \\ \hline [PERSON] and [PERSON] (2024) [19] & RST in vector spaces & Provided theoretical foundations for applying RST in finite-dimensional vector spaces. \\ \hline [PERSON] and [PERSON] (2024) [20] & Disease prediction & Achieved high accuracy in predicting neurodevelopmental diseases using RST and machine learning. \\ \hline [PERSON] and [PERSON] (2025) [21] & Student performance prediction & Optimized feature mining using entropy-weighted RST for predicting student performance in education. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related works in Rough Set Theory. Advantage over Existing Methods: Unlike traditional dependency-based reduct algorithms, MLSpecialReduct **dynamically optimizes** the attribute selection process, ensuring minimal redundancy and maximal relevance. This makes it highly efficient for **high-dimensional datasets** where traditional methods may fail to scale. * **Novelty**: MLFuzzyRoughSet extends traditional RST by integrating **fuzzy logic*
* to handle uncertainty and imprecision in data. It approximates decision classes using **fuzzy lower and upper approximations**, enabling robust feature selection in datasets with continuous or noisy attributes.