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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 6 Documents
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Search results for , issue "Vol. 13 No. 3 (2019): Sep: Enterprise Modelling" : 6 Documents clear
Advancements in Optimizing Fuzzy Grid Partition for Enhanced Rule Generation Performance: Algorithms, Interpretability, and Scalability Nazarshoev Shofakirova; Tojiniso Khorg
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: 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.v13i3.13

Abstract

This research focuses on optimizing fuzzy grid partitioning to enhance rule generation performance in fuzzy rule-based systems. A novel mathematical formulation is proposed, aiming to minimize the number of fuzzy grid cells while considering coverage, regularity, and overlap constraints. The study demonstrates the effectiveness of the approach through a case example in credit risk assessment. The optimized fuzzy grid partitioning scheme generates concise and interpretable fuzzy rules, improving the accuracy and interpretability of the rule-based system. The research highlights the significance of interpretability in rule-based systems and showcases the scalability and applicability of the approach across various domains. However, limitations include the lack of comprehensive comparisons, limited exploration of generalizability to different datasets, and the need for real-world implementation considerations. Nonetheless, this research provides valuable insights into optimizing fuzzy grid partitioning for rule generation and contributes to the advancement of fuzzy rule-based systems in decision support and problem-solving tasks. Future work should address the identified limitations and explore the practical implementation of the approach.
Scalable and Adaptive Fuzzy Grid Partitioning for Enhanced Rule Generation in Complex Decision-Making Systems Abubakar Gwarzo Ɗambatta
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: 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.v13i3.14

Abstract

This research focuses on addressing the challenges of rule generation in complex decision-making systems by proposing a scalable and adaptive fuzzy grid partitioning approach. Traditional rule generation methods often struggle to handle large datasets and dynamic environments, leading to decreased accuracy and computational inefficiencies. In this study, we present a novel approach that integrates scalable and adaptive techniques to enhance the accuracy, efficiency, and interpretability of rule-based frameworks. The scalable fuzzy grid partitioning algorithm efficiently partitions the attribute space, allowing for the generation of rules in decision-making systems with a large number of data points. By incorporating data parallelization and dimensionality reduction techniques, the approach mitigates computational complexity while maintaining rule generation accuracy. Furthermore, the adaptive fuzzy grid partitioning algorithm dynamically adjusts the partitioning structure based on changing conditions, capturing evolving patterns and ensuring the relevancy and reliability of the generated rules over time. The generated rules are evaluated using fuzzy rule evaluation functions, which consider the degree of membership in the corresponding fuzzy grid cells. This evaluation process ranks and selects the rules based on their firing strengths, providing an interpretable decision-making framework for complex systems. The approach enhances the interpretability of the generated rules by capturing the uncertainties and complexities inherent in decision-making processes. To validate the effectiveness of the proposed approach, we conducted experiments using a credit risk assessment case example. The results demonstrate improved accuracy and efficiency compared to traditional rule generation methods. The generated rules offer transparency and insight into the factors influencing credit risk assessments, enabling informed decision-making. However, this research has some limitations, including potential dataset dependencies, the choice of fuzzy membership functions, computational complexity, and the need for further evaluation metrics and real-world implementation considerations. Future research should focus on addressing these limitations and exploring the applicability of the proposed approach in diverse domains. In conclusion, the scalable and adaptive fuzzy grid partitioning approach presented in this research offers a promising solution to the challenges of rule generation in complex decision-making systems. By addressing scalability, adaptability, and interpretability, this approach enhances the accuracy and efficiency of rule-based frameworks, paving the way for more effective decision support systems in various domains.
A Novel Hybrid Approach: Grid Partition and Rough Set-Based Fuzzy Rule Generation for Accurate Dataset Classification Dalzon Marie; Moïse Etzer
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: 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.v13i3.15

Abstract

Accurate dataset classification is a fundamental task in various domains such as machine learning, pattern recognition, and data mining. This research proposes a novel hybrid approach that combines grid partitioning, rough set-based feature reduction, and fuzzy rule generation to enhance classification accuracy and interpretability. The approach begins with the partitioning of the dataset into a grid of cells, enabling localized analysis and capturing intricate patterns. Next, rough set-based feature reduction is applied to identify essential features and reduce dimensionality. This process helps overcome the curse of dimensionality commonly associated with complex datasets. Subsequently, fuzzy rule generation is employed, leveraging linguistic variables and membership functions to represent imprecise and uncertain information. This enhances interpretability by providing transparent decision-making rules. To evaluate the effectiveness of the proposed approach, comparative analysis with traditional classification methods, including decision trees, support vector machines, and neural networks, is conducted. The results demonstrate the superiority or at least comparability of the hybrid approach in terms of classification accuracy, computational complexity, and interpretability. However, it is essential to acknowledge the limitations of the research, such as the sensitivity to grid size and the interpretability-performance trade-off. Future research can focus on refining the approach by exploring optimal grid size selection methods and mitigating the interpretability-performance trade-off.The findings of this research contribute to the advancement of accurate dataset classification techniques. The proposed hybrid approach offers improved classification accuracy, handles complex datasets effectively, and enhances interpretability through fuzzy rules. The practical implications of the research span domains such as bioinformatics, IoT, and financial analysis. Overall, this research provides a foundation for further exploration, refinement, and real-world applications of the hybrid approach in accurate dataset classification scenarios.
Hybridizing Grid Partitioning and Rough Set Method for Fuzzy Rule Generation: A Robust Framework for Dataset Classification with Enhanced Interpretability and Scalability Philippe Brusselen Del Élisabethville; Milongwe Del Norte
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: 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.v13i3.16

Abstract

This research presents a novel approach, called GP-RS-FRG, that combines grid partitioning and rough set method for fuzzy rule generation in dataset classification. The aim is to enhance interpretability and scalability while maintaining accuracy in the classification process. Traditional classification methods often lack transparency, making it difficult to interpret their decisions, especially with complex datasets. Additionally, these methods may face challenges in handling large datasets with numerous attributes and instances. The proposed framework addresses these limitations by generating transparent and understandable fuzzy rules. The GP-RS-FRG framework utilizes grid partitioning to divide the input space into non-overlapping grid cells, reducing the search space and improving computational efficiency. By integrating the rough set method, the framework identifies the most significant attributes, reducing redundancy and simplifying the rule base. This enhances interpretability and simplifies the decision-making process. The generated fuzzy rules capture the complex relationships between attributes and classes, providing meaningful insights into the classification model. Experimental evaluation on diverse datasets demonstrates the effectiveness of the GP-RS-FRG framework in generating accurate fuzzy rules while maintaining interpretability and scalability. The framework enables domain experts to understand and interpret the classification process, facilitating informed decision-making. It has potential applications in various domains where transparent and scalable classification models are required. Future research directions may include exploring alternative approaches, variations, or refinements to further enhance the framework's performance. Comparative studies and experiments on larger and more diverse datasets would provide a deeper understanding of its capabilities and limitations. The generalizability and applicability of the framework to different domains should also be investigated to promote wider adoption and impact.
Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification: Enhancing Accuracy and Interpretability for Complex Data Analysis Tafitarisoa Solofo; Jelca Velo Norlestine Jérôme
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: 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.v13i3.17

Abstract

This research proposes a novel approach that combines hybrid grid partitioning, fuzzy rule generation, and rough set theory to enhance the accuracy and interpretability of dataset classification in complex data analysis. The study addresses the limitations of traditional classification methods by leveraging grid partitioning to simplify the dataset representation and focus on relevant regions of the attribute space. Fuzzy rule generation captures uncertainties and enables a more nuanced classification by considering membership degrees. Additionally, rough set theory is employed to identify relevant attributes, reducing the complexity of the model and enhancing interpretability. The proposed approach is particularly suitable for complex datasets characterized by high dimensionality and uncertainties. Experimental evaluations demonstrate its effectiveness in improving accuracy and providing meaningful insights for decision-making. The research contributes to advancing the field of dataset classification by offering a comprehensive framework that combines grid partitioning, fuzzy rule generation, and rough set theory to tackle complex data analysis challenges.
Fuzzy Rules for Data Set Classification: A Hybrid Approach Using Rough Set and Grid Partitioning Daniachew, Adeola Azy; Clevon, Averey Barack; Avram, Abimelech Keita; Chislon, Dodavah Tesseman
International Journal of Enterprise Modelling Vol. 13 No. 3 (2019): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v13i3.73

Abstract

This research aims to address the issue of exponential rule generation in fuzzy rule-based classification systems by developing a hybrid grid partition and rough set method. Fuzzy rule-based classification systems have the potential to construct linguistically understandable models, but a major constraint is the significant increase in the number of rules with a high number of attributes, which can diminish interpretation and classification accuracy. In this study, the grid partition method is utilized to generate fuzzy rules with adaptively adjusted grid structures, thus avoiding exponential rule proliferation. The research encompasses the use of the Iris Flower dataset, rule formation while considering variable precision, and classification accuracy testing. The research findings indicate that the hybrid grid partition and rough set method produces more efficient and accurate fuzzy rules, with a classification accuracy rate of 83.33%. This method also successfully reduces the number of generated rules, making it a promising solution to tackle the issue of exponential rule increase in fuzzy rule-based classification systems. The conclusions of this research can be described based on the findings, discussions and results above are: The application of the rough set method at the beginning of rule formation can reduce the number of condition attributes and the number of redundant objects so that the rule formation process becomes more concise The grid partition method with a grid structure applying adapted techniques produces fuzzy rules that have the potential to be generated. The hybrid grid partition method and rough set method produce classification rules that do not increase exponentially. The number of classification rules generated decreases as the number of condition attributes and the number of objects classified decrease. Fuzzy rules generated by the hybrid method produce a classification accuracy rate of 83.3% with 9 data records and the number of unclassified data is 0.

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