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International Journal of Enterprise Modelling

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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 109 Documents

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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

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

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

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

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

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.

Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification Abubakullo, Abubakullo; Alesha, Aisyah
International Journal of Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances the representation and separation of data regions, improving classification accuracy. The generated fuzzy rule base provides interpretable decision rules, enabling domain experts to gain insights into the classification process. The proposed approach strikes a balance between accuracy and interpretability, making it valuable for various domains. However, limitations such as generalizability and scalability should be considered. Comparative analysis with existing methods and real-world case studies would further validate the effectiveness of the approach. Overall, this research contributes to the advancement of dataset classification and provides a novel integrated approach for accurate and interpretable classification.

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory Ferguson, Tokpa Braxton
International Journal of Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research presents an integrated approach for fuzzy rule generation in dataset classification by combining hybrid grid partitioning and rough set theory. The objective is to enhance the accuracy and interpretability of classification models. The approach leverages hybrid grid partitioning to achieve localized rule generation, capturing the local characteristics and patterns within different regions of the feature space. Furthermore, rough set theory is applied for attribute reduction, identifying the most relevant features and reducing the complexity of the classification problem. The generated fuzzy rules provide interpretable and understandable classification rules that facilitate domain expert interpretation. The research contributes to the field by proposing a comprehensive framework that improves both accuracy and interpretability of dataset classification. The findings demonstrate the effectiveness of the integrated approach, although certain limitations exist. Future research should focus on parameter selection, scalability challenges, and the applicability of the approach to diverse problem domains. The integrated approach presents a promising methodology for enhancing the accuracy and interpretability of dataset classification, with potential applications in various domains where accurate and interpretable classification models are crucial.

Hybridizing grid partitioning, rough set theory, and feature selection for fuzzy rule generation in dataset classification Lawrence, Ogange
International Journal of Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research investigates the hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation in Dataset Classification. The objective is to improve classification accuracy and interpretability by integrating multiple techniques. Grid partitioning is employed to divide the dataset into regions, allowing localized analysis. Rough set theory is utilized for attribute reduction and feature selection, identifying informative features within each region. Fuzzy rule generation is applied to generate interpretable classification rules using linguistic terms and membership functions. The hybrid model is optimized using metaheuristic algorithms to maximize classification performance. The research demonstrates the potential of the hybrid approach through experiments on the Iris flower dataset. The findings reveal improved classification accuracy, enhanced interpretability, and effective handling of complex datasets. The research contributes to the field by integrating these techniques into a cohesive framework and highlights the importance of parameter settings, computational complexity, and real-world applications. Future work should address these limitations and validate the approach on diverse datasets. The hybridization of Grid Partitioning, Rough Set Theory, and Feature Selection for Fuzzy Rule Generation holds promise for advancing classification models in various domains

Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification Joseph, Luke; O'Leary, Meiser Llywellenie; Zagré, Bisani
International Journal of Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i2.21

Accurate dataset classification is a critical task in various domains, and combining different methodologies can enhance classification performance. This research presents a novel approach that integrates Hybrid Grid Partition and Rough Set methods for fuzzy rule generation, aiming to improve accuracy and interpretability in dataset classification. The proposed approach leverages Hybrid Grid Partition to discretize continuous attributes and Rough Set attribute reduction to identify essential attributes, enabling accurate classification while handling uncertainty and imprecision. The generated fuzzy rules provide interpretability, aiding decision-making processes and providing insights into classification factors. The approach's robustness and generalization capabilities are demonstrated through experiments on diverse datasets, indicating its potential applicability in real-world scenarios. However, limitations such as the absence of specific evaluation metrics and the need for further validation on larger datasets are acknowledged. Overall, this research contributes to accurate dataset classification by offering a novel integrated approach and highlighting areas for future investigation and refinement

Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation Velo, Randrianja; Tamatave, Jérôme; Sahambala, Solofo
International Journal of Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research presents a novel approach for optimizing dataset classification through the integration of a hybrid grid partition and rough set method for fuzzy rule generation. The objective is to improve classification accuracy and interpretability while effectively handling uncertainty in the dataset. The proposed approach combines grid partitioning, rough set theory, and fuzzy logic to identify relevant attributes within each grid cell, generate accurate fuzzy rules, and perform classification based on fuzzy inference. The research demonstrates the improved accuracy of the hybrid approach compared to traditional methods, along with enhanced interpretability of the generated fuzzy rules. The scalability and generalizability of the approach are validated through its application to a case example in customer churn prediction in the telecommunications industry. However, certain limitations, such as the selection of the partitioning scheme, computational complexity, and handling of missing data, need to be considered. Further research is required to address these limitations and benchmark the approach against state-of-the-art techniques. The proposed hybrid approach contributes to the field of dataset classification by offering an effective and interpretable methodology for improved classification performance and actionable insights in real-world applications

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Zeehimin Huang Ping

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Zeehimin Huang Ping

Contact Email internationalenterpriseintegra@gmail.com

Phone +6281360000791

Journal Mail Official internationalenterpriseintegra@gmail.com

Editorial Address Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location Kota jayapura, P a p u a INDONESIA

Abstract

Abstract

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Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Zeehimin Huang Ping

Contact Email
internationalenterpriseintegra@gmail.com

Phone
+6281360000791

Journal Mail Official
internationalenterpriseintegra@gmail.com

Editorial Address
Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location
Kota jayapura,

P a p u a

INDONESIA