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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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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

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

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

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

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

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.

Enhancing Rule Generation in Dataset Classification through Optimized Fuzzy Grid Partitioning: A Performance Optimization Framework Mohammad Nor Amin Hasnan
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

The research aims to enhance rule generation in dataset classification through an optimized fuzzy grid partitioning framework. The proposed framework combines fuzzy grid partitioning, which captures uncertainty and ambiguity in datasets, with an optimization algorithm to improve the accuracy and efficiency of rule generation. The main contributions of this research include enhanced accuracy in dataset classification, improved computational efficiency, and emphasis on interpretability and transparency of the generated rules. The framework achieves improved accuracy by optimizing the fuzzy grid partitioning process to capture underlying patterns in the data. The incorporation of an optimization algorithm reduces the computational complexity, enabling faster classification on large-scale datasets. The generated rules are designed to be understandable to domain experts, enhancing transparency and facilitating decision-making. The research acknowledges certain limitations, such as algorithm dependence and the need for real-world validation. Future research directions include exploring alternative optimization algorithms, conducting extensive evaluations on diverse datasets, and validating the framework's performance in real-world applications. Overall, the proposed framework offers a valuable contribution to the field of data mining and machine learning, providing an effective approach to enhance rule generation in dataset classification.

Optimizing Production Planning in Uncertain Environments: A Fuzzy Goal Programming Approach with Adaptive Metaheuristic Algorithms Jose Sanchez; Gotzee Marion
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research focuses on the optimization of production planning in uncertain environments by employing a fuzzy goal programming approach with adaptive metaheuristic algorithms. Uncertainty poses significant challenges in production planning, requiring robust methodologies to handle conflicting objectives and unpredictable factors. In this study, we propose a novel approach that integrates fuzzy logic and metaheuristic algorithms to address these challenges effectively. The fuzzy goal programming framework enables the modeling of imprecise or vague goals and constraints, providing a more accurate representation of uncertainties. The adaptive metaheuristic algorithms offer efficient optimization capabilities by exploring the solution space and adapting to changing circumstances. A mathematical formulation is developed, considering multiple objectives such as minimizing production costs, maximizing production output, minimizing inventory levels, and minimizing deviations from customer demand. The formulation is solved using appropriate metaheuristic algorithms, such as genetic algorithms. A numerical example and a case study are presented to demonstrate the practical application and effectiveness of the proposed approach. The results show that the approach successfully optimizes production planning, achieving the desired levels of satisfaction for each objective in uncertain environments. This research contributes to the field by providing decision-makers with a comprehensive and robust methodology to improve production planning strategies, enhance operational efficiency, and meet customer demands effectively in the face of uncertainty.

Optimizing Production Planning Under Uncertainty: A Fuzzy Goal Programming Approach with Adaptive Metaheuristic Algorithms for Improved Decision-Making Managua Jaramillo Luther; Escritura Perezalonso Según
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research addresses the challenge of uncertainty in production planning by proposing a novel approach that combines fuzzy goal programming with stochastic optimization techniques. The integration of these two methodologies provides decision-makers with a comprehensive framework to make improved and robust decisions in the face of uncertainty. Fuzzy goal programming allows decision-makers to express imprecise objectives and constraints, accommodating the inherent vagueness and trade-offs in production planning. Stochastic optimization techniques consider multiple scenarios and their associated probabilities, enabling the optimization of production plans that are robust and near-optimal across different uncertain situations. The proposed approach offers flexibility in decision-making, as decision-makers can express their preferences and goals in a subjective manner while considering uncertainty. The research contributes to the field by providing a systematic framework to manage uncertainty in production planning and improve overall performance in manufacturing organizations. The practical implications of the research are significant, as decision-makers can make informed decisions regarding resource allocation, customer demand fulfillment, and cost optimization. The research findings highlight the effectiveness of the proposed approach and its potential for application in various industry contexts. However, limitations in computational complexity and the need for further refinements are acknowledged. Future research can focus on refining the approach, addressing specific industry challenges, and extending its applicability in real-world production planning problems.

Hybrid Grid Partitioning and Rough Set Method for Fuzzy Rule Generation: Enhancing Dataset Clustering and Interpretability in Data Analysis Nordiyya Ellwanger Hamerkaz
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research focuses on the development of a hybrid approach that combines grid partitioning, rough set theory, and fuzzy rule generation to enhance dataset clustering accuracy and improve the interpretability of generated rules in data analysis. The integration of grid partitioning techniques improves clustering accuracy by reducing the search space and efficiently identifying data patterns and relationships. Incorporating rough set theory facilitates attribute reduction, reducing the dimensionality of the dataset and enhancing interpretability. Fuzzy rule generation enables linguistic representation, allowing for human-understandable explanations. The proposed approach addresses the limitations of traditional methods, providing a comprehensive framework for accurate clustering and interpretable rule generation. The significance and potential benefits of the approach are discussed, along with its limitations and future directions. Numerical examples and findings demonstrate the effectiveness of the hybrid approach in enhancing dataset clustering accuracy and interpretability. The research contributes to advancing the field of data analysis by providing a comprehensive framework for accurate and interpretable analysis of complex datasets.

Hybrid Grid Partitioning and Rough Set Method for Enhanced Dataset Clustering and Interpretable Rule Generation in Big Data Analysis Miankoff Gordion JKacker
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research introduces a novel approach that combines hybrid grid partitioning and rough set theory for enhanced dataset clustering and interpretable rule generation in big data analysis. The proposed method addresses the challenges of scalability, high dimensionality, and interpretability, which are common in analyzing large and complex datasets. The hybrid approach leverages grid partitioning to efficiently handle large datasets by dividing them into manageable subsets. This enables parallel processing and reduces computational complexity. Additionally, rough set theory is incorporated to identify essential attributes that contribute to cluster formation, thereby reducing the dimensionality of the data and enhancing clustering accuracy. One of the key contributions of this research is the generation of interpretable rules based on the clustering results. By applying rough set-based attribute selection, the method identifies the crucial attributes that determine cluster assignments. These interpretable rules provide valuable insights into the relationships between attributes and clusters, aiding in understanding the underlying patterns in the data. A numerical example is provided to demonstrate the effectiveness of the proposed method. The results show improved clustering accuracy and the generation of clear and interpretable rules based on the dataset attributes. While the research presents significant advancements, it is important to consider the limitations, including potential challenges in generalizability, sensitivity to parameter settings, and computational complexity. Future research should focus on further validation and evaluation of the method on diverse datasets and comparisons with other state-of-the-art clustering algorithms. In conclusion, the hybrid grid partitioning and rough set method offer a promising solution for enhanced dataset clustering and interpretable rule generation in big data analysis. The research contributes to the advancement of data analytics methodologies and provides practical approaches for extracting knowledge from complex datasets, supporting decision-making processes, and enabling better understanding of underlying data patterns.

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Zeehimin Huang Ping

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internationalenterpriseintegra@gmail.com

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+6281360000791

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internationalenterpriseintegra@gmail.com

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

Location
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P a p u a

INDONESIA

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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

Abstract

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