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International Journal of Enterprise Modelling
Published by International Enterprise Integration Association
ISSN : 16939220     EISSN : 29878713     DOI : https://doi.org/10.35335/emod
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation
The International Journal of Enterprise Modelling serves as a venue for anyone interested in business and management modelling. It investigates the conceptual forerunners and theoretical underpinnings that lead to research modelling procedures that inform research and practice.
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
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Search results for , issue "Vol. 14 No. 1 (2020): Jan: Enterprise Modelling" : 5 Documents clear
Optimizing Dataset Classification with Hybrid Grid Partitioning and Rough Set-based Fuzzy Rule Generation Approach Dall Matew Caloz
International Journal of Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i1.23

Abstract

This research focuses on optimizing dataset classification by combining hybrid grid partitioning and rough set-based fuzzy rule generation. Traditional classification algorithms often face challenges in handling high-dimensional data, attribute redundancy, and uncertainty, leading to reduced accuracy and increased computational complexity. To address these issues, we propose an integrated approach that leverages hybrid grid partitioning for adaptive representation of the dataset, rough set-based attribute reduction for identifying relevant attributes, and fuzzy rule generation for handling uncertainty and capturing complex relationships. The hybrid grid partitioning creates multiple levels of granularity, capturing both local and global patterns. Rough set-based attribute reduction reduces dimensionality and eliminates redundant information. Fuzzy rule generation enables the handling of uncertainty and the mapping of input attributes to output classes. We present a case example of customer churn prediction in a telecommunications company to illustrate the practical relevance of the proposed approach. The optimized classification model provides insights into churn factors and enables proactive measures for customer retention. The research contributes to the field by offering an integrated framework to enhance classification accuracy, interpretability, and efficiency. The findings have the potential to benefit various industries and applications that rely on accurate classification models, improving decision-making processes and performance of intelligent systems.
Hybrid Grid Partitioning and Rough Set-based Fuzzy Rule Generation for Enhanced Accuracy and Interpretability in Dataset Classification Philip Shukula Murphy
International Journal of Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i1.24

Abstract

Achieving a balance between accuracy and interpretability is a critical challenge in dataset classification. Traditional methods often prioritize accuracy but lack interpretability, making it difficult to comprehend and trust the classification decisions. In this research, we propose a hybrid approach that combines grid partitioning, rough set-based attribute reduction, and fuzzy rule generation to enhance both the accuracy and interpretability in dataset classification. The grid partitioning technique discretizes continuous attribute values into intervals, reducing complexity and improving accuracy. The rough set-based attribute reduction identifies the most relevant attributes, reducing noise and irrelevant information. Fuzzy rule generation generates human-understandable rules based on the refined attribute values and rough sets, providing explanations for classification decisions. By integrating these techniques, our approach strikes a balance between accuracy and interpretability. The weight parameter in the objective function allows customization according to specific requirements. Experimental evaluations demonstrate the effectiveness of the proposed approach in achieving enhanced accuracy and interpretability. The hybrid approach has potential applications in domains where transparent and understandable classification models are crucial, such as medical diagnosis or decision support systems. Further research can explore parameter optimization and evaluate the approach on diverse datasets to enhance its applicability and robustness.
Optimizing Fuzzy Grid Partition Performance for Rule Generation Essowèmlou Jürgen
International Journal of Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i1.25

Abstract

This research focuses on optimizing the performance of fuzzy grid partitioning for rule generation. Fuzzy grid partitioning is a widely used technique for extracting rules from data, but it faces challenges related to computational complexity, rule redundancy, rule quality, grid size determination, and interpretability. To address these challenges, we propose a mathematical formulation and explore techniques to enhance the efficiency and effectiveness of the rule generation process.The research aims to minimize computational complexity by introducing parallel processing and dynamic grid density adjustment methods. By reducing the time required for grid generation and rule generation, we enable the application of fuzzy grid partitioning to larger datasets. To improve rule quality, we investigate techniques to reduce rule redundancy and enhance rule quality metrics such as accuracy, precision, or coverage. This ensures the generation of accurate and meaningful rules that capture the underlying patterns in the data. Grid size determination is a critical aspect of fuzzy grid partitioning. We explore techniques to determine the optimal grid size, striking a balance between granularity and computational efficiency. This enables the capturing of important patterns in the data while avoiding excessive computational complexity. Interpretability is vital for the acceptance and utilization of generated rules. We propose methods to minimize interpretability measures such as rule length or linguistic complexity, resulting in concise and understandable rule sets. Real-world datasets are used to evaluate the proposed techniques and algorithms, demonstrating their applicability and generalizability. The research outcomes have practical implications in domains such as data mining, machine learning, and expert systems. In conclusion, this research contributes to the optimization of fuzzy grid partitioning for rule generation. The proposed techniques enhance the efficiency, effectiveness, scalability, and interpretability of rule-based systems. The findings empower researchers and practitioners to generate high-quality and interpretable rule sets from large and complex datasets.
Optimizing Fuzzy Grid Partition Performance for Rule Generation in Dataset Classification Ülikool Gilad
International Journal of Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i1.26

Abstract

This research focuses on optimizing fuzzy grid partitioning for rule generation in dataset classification. Fuzzy grid partitioning is a technique used to divide the feature space into fuzzy regions, which are subsequently used to generate rules for classification. However, the existing implementation of fuzzy grid partitioning suffers from limitations related to grid size determination, adaptability to data distributions, computational efficiency, and rule quality. To address these challenges, we formulate a mathematical problem and propose an optimization process that iteratively adjusts the grid size, performs fuzzy grid partitioning, generates rules, prunes them for relevance, and evaluates the resulting rule-based classifier. The optimization aims to find an optimal balance between accuracy and efficiency while ensuring that the generated rules meet the desired performance thresholds. Through a numerical example, we demonstrate the effectiveness of the optimization process, showcasing how it produces an accurate and efficient rule-based classifier. This research contributes to advancements in fuzzy grid partitioning for rule generation, improving the accuracy, efficiency, and interpretability of rule-based classifiers. It opens avenues for further investigation and refinement of the optimization techniques, enabling better dataset classification in various domains where transparent decision-making processes are essential.
Enhancing Dataset Classification through Optimized Fuzzy Grid Partitioning for Rule Generation Ndayishimi Nijimbere
International Journal of Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i1.27

Abstract

This research focuses on optimizing the performance of fuzzy grid partitioning for rule generation in dataset classification. The objective is to develop an approach that improves classification accuracy while maintaining interpretability and considering practical constraints. The research introduces a novel optimization framework that balances accuracy and complexity through an objective function. Fuzzy sets and a grid structure are defined, and a rule base is generated based on the fuzzy grid and classification outcomes. The proposed approach demonstrates enhanced classification accuracy compared to traditional methods, capturing underlying patterns effectively. Additionally, the approach achieves improved interpretability by incorporating complexity constraints. The research addresses scalability and compares the approach with existing techniques. The findings contribute to the field of rule-based classifiers, providing insights into accurate and interpretable classification models with practical applicability in various domains. Future research directions include generalizability, parameter sensitivity, and comparison with state-of-the-art techniques.

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