A New Modeling to Feature Selection Based on the Fuzzy Rough Set Theory in Normal and Optimistic States on Hybrid Information Systems
arXiv:2603.08900v1 Announce Type: new Abstract: Considering the high volume, wide variety, and rapid speed of data generation, investigating feature selection methods for big data presents various applications and advantages. By removing irrelevant and redundant features, feature selection reduces data dimensions, thereby facilitating optimal decision-making within decision systems. One of the key tools for feature selection in hybrid information systems is fuzzy rough set theory. However, this theory faces two significant challenges: First, obtaining fuzzy equivalence relations through intersection operations in high-dimensional spaces can be both time-consuming and memory-intensive. Additionally, this method may produce noisy data, complicating the feature selection process. The purpose and innovation of this paper are to address these issues. We proposed a new feature selection model that calculates the combined distance between objects and subsequently used this information to der
arXiv:2603.08900v1 Announce Type: new Abstract: Considering the high volume, wide variety, and rapid speed of data generation, investigating feature selection methods for big data presents various applications and advantages. By removing irrelevant and redundant features, feature selection reduces data dimensions, thereby facilitating optimal decision-making within decision systems. One of the key tools for feature selection in hybrid information systems is fuzzy rough set theory. However, this theory faces two significant challenges: First, obtaining fuzzy equivalence relations through intersection operations in high-dimensional spaces can be both time-consuming and memory-intensive. Additionally, this method may produce noisy data, complicating the feature selection process. The purpose and innovation of this paper are to address these issues. We proposed a new feature selection model that calculates the combined distance between objects and subsequently used this information to derive the fuzzy equivalence relation. Rather than directly solving the feature selection problem, this approach reformulates it into an optimization problem that can be tackled using appropriate meta-heuristic algorithms. We have named this new approach FSbuHD. The FSbuHD model operates in two modes - normal and optimistic - based on the selection of one of the two introduced fuzzy equivalence relations. The model is then tested on standard datasets from the UCI repository and compared with other algorithms. The results of this research demonstrate that FSbuHD is one of the most efficient and effective methods for feature selection when compared to previous methods and algorithms.
Executive Summary
This study proposes a novel feature selection model, FSbuHD, based on the fuzzy rough set theory to address challenges in high-dimensional spaces of hybrid information systems. FSbuHD calculates the combined distance between objects and uses this information to derive the fuzzy equivalence relation, reformulating the feature selection problem as an optimization task. The model operates in two modes - normal and optimistic - and is tested on standard datasets from the UCI repository. The results demonstrate FSbuHD's efficiency and effectiveness in feature selection compared to previous methods and algorithms. The study contributes to the development of robust feature selection techniques for big data applications, with potential applications in decision-making and data analysis.
Key Points
- ▸ FSbuHD is a novel feature selection model based on fuzzy rough set theory to address challenges in high-dimensional spaces.
- ▸ The model calculates the combined distance between objects and uses this information to derive the fuzzy equivalence relation.
- ▸ FSbuHD operates in two modes - normal and optimistic - and is tested on standard datasets from the UCI repository.
Merits
Strength in Addressing High-Dimensional Challenges
FSbuHD effectively addresses the challenges of obtaining fuzzy equivalence relations in high-dimensional spaces, providing a practical solution for big data applications.
Demerits
Limited Evaluation on Real-World Applications
The study primarily evaluates FSbuHD on standard datasets from the UCI repository, and its performance on real-world applications and datasets remains to be seen.
Expert Commentary
The study proposes a novel feature selection model, FSbuHD, which demonstrates promising results in addressing challenges in high-dimensional spaces. However, the model's performance on real-world applications and datasets remains to be seen. The study's contributions to the development of robust feature selection techniques for big data applications are significant, with potential applications in decision-making and data analysis. Furthermore, the study's findings may inform policy decisions on data management and analysis in various domains.
Recommendations
- ✓ Future studies should evaluate FSbuHD on real-world applications and datasets to demonstrate its practicality and effectiveness.
- ✓ The study's findings should be disseminated to the broader research community to inform the development of robust feature selection techniques for big data applications.