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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. 17 No. 2 (2023): May: Enterprise Modelling" : 5 Documents clear
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.741 KB) | DOI: 10.35335/emod.v17i2.18

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.976 KB) | DOI: 10.35335/emod.v17i2.19

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.668 KB) | DOI: 10.35335/emod.v13i1.20

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.893 KB) | DOI: 10.35335/emod.v17i2.22

Abstract

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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