International Journal of Enterprise Modelling最新文献_第3页

Enhancing Accuracy and Interpretability in Dataset Classification: Advancements in Hybrid Grid Partition and Rough Set Methods for Fuzzy Rule Generation 提高数据集分类的准确性和可解释性:用于模糊规则生成的混合网格划分和粗糙集方法的进展

International Journal of Enterprise Modelling Pub Date : 2018-12-30 DOI: 10.35335/emod.v13i1.5

Josea Moreno Chawla, Herrera Rocío

{"title":"Enhancing Accuracy and Interpretability in Dataset Classification: Advancements in Hybrid Grid Partition and Rough Set Methods for Fuzzy Rule Generation","authors":"Josea Moreno Chawla, Herrera Rocío","doi":"10.35335/emod.v13i1.5","DOIUrl":"https://doi.org/10.35335/emod.v13i1.5","url":null,"abstract":"Accurate and interpretable classification of datasets plays a crucial role in various domains, including healthcare, finance, and image recognition. This research focuses on enhancing accuracy and interpretability in dataset classification through the integration of hybrid grid partition and rough set methods for fuzzy rule generation. The proposed mathematical model leverages the grid partition approach to handle the curse of dimensionality and reduce dataset complexity, while the rough set method identifies essential features and generates meaningful fuzzy rules. The assigned membership values to linguistic terms further enhance interpretability. The model's accuracy and interpretability were evaluated using a diabetes dataset, achieving an accuracy rate of 85% on the validation dataset and 83% on the testing dataset. Comparative analysis demonstrated competitive performance against existing methods. The iterative refinement process contributed to the model's optimization. However, limitations include dataset dependency, parameter sensitivity, and scalability. Future research directions include advanced rule pruning techniques, optimization of model parameters, handling imbalanced datasets, incorporating feature selection, robustness and scalability evaluation, comparative studies, and real-world application validation. The proposed model presents a promising approach to enhance accuracy and interpretability in dataset classification.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123901627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Grid Partitioning And Rough Set Method Approach For Fuzzy Rule Generation 模糊规则生成的网格划分与粗糙集方法

International Journal of Enterprise Modelling Pub Date : 2018-12-30 DOI: 10.35335/emod.v13i1.6

Chris Kornelisius, Eyvan Caeyso, Ching-Ghiang Feh

{"title":"Grid Partitioning And Rough Set Method Approach For Fuzzy Rule Generation","authors":"Chris Kornelisius, Eyvan Caeyso, Ching-Ghiang Feh","doi":"10.35335/emod.v13i1.6","DOIUrl":"https://doi.org/10.35335/emod.v13i1.6","url":null,"abstract":"The generation of accurate and interpretable fuzzy rules plays a crucial role in various data analysis and decision-making systems. In this research, we propose a mathematical model based on grid partitioning and the rough set method for fuzzy rule generation. The model combines the advantages of grid partitioning, which enables localized analysis, and the rough set method, which captures the uncertainty in the dataset. By partitioning the input space into grids and determining the lower and upper approximations within each grid, the model generates accurate and representative fuzzy rules. These rules provide meaningful insights into the relationships between input variables and output variables, enhancing interpretability. The model is applied in a case example of temperature control to demonstrate its effectiveness. Additionally, a numerical example showcases the predictive performance and applicability of the model. The limitations of the research, such as dependency on data quality and scalability issues, are also discussed. Despite these limitations, the mathematical model contributes to the field of data analysis and decision-making systems by offering an approach that integrates grid partitioning and rough set method for fuzzy rule generation. It holds promise for applications in various domains, providing accurate and interpretable fuzzy rules for decision support systems and intelligent automation.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125166810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification: Enhancing Accuracy and Interpretability 混合网格划分和粗糙集方法在数据集分类中的模糊规则生成:提高准确性和可解释性

International Journal of Enterprise Modelling Pub Date : 2018-12-30 DOI: 10.35335/emod.v13i1.4

Norquist Da Silva, Gregor Bogard Hohpe

{"title":"Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification: Enhancing Accuracy and Interpretability","authors":"Norquist Da Silva, Gregor Bogard Hohpe","doi":"10.35335/emod.v13i1.4","DOIUrl":"https://doi.org/10.35335/emod.v13i1.4","url":null,"abstract":"This research presents a hybrid grid partition and rough set method for fuzzy rule generation in dataset classification, aiming to enhance accuracy and interpretability. The proposed mathematical model combines grid partitioning, rough set theory, and fuzzy logic to identify relevant attributes, reduce dimensionality, and generate interpretable fuzzy rules. The model is evaluated using a case example of iris flower classification and demonstrates competitive accuracy in predicting the species of iris flowers based on their attributes. The interpretability of the generated fuzzy rules provides transparent explanations for the classification decisions, allowing domain experts to understand and interpret the reasoning behind the predictions. Comparative analysis with traditional algorithms showcases the superiority of the hybrid model in terms of accuracy and interpretability. Sensitivity analysis enables parameter tuning and customization, further improving the model's performance. The practical implications of the hybrid model are discussed, and its potential applications in various domains are highlighted. The research concludes that the hybrid grid partition and rough set method offer an effective approach for accurate and interpretable dataset classification, with implications for decision-making and insights in real-world applications.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Fuzzy Rule Generation: A Grid Partitioning and Rough Set Method Approach for Enhanced Accuracy and Interpretability 优化模糊规则生成:提高准确性和可解释性的网格划分和粗糙集方法

International Journal of Enterprise Modelling Pub Date : 2018-12-30 DOI: 10.35335/emod.v13i1.7

Aisyah Alesha

{"title":"Optimizing Fuzzy Rule Generation: A Grid Partitioning and Rough Set Method Approach for Enhanced Accuracy and Interpretability","authors":"Aisyah Alesha","doi":"10.35335/emod.v13i1.7","DOIUrl":"https://doi.org/10.35335/emod.v13i1.7","url":null,"abstract":"This research focuses on optimizing fuzzy rule generation through the application of grid partitioning and rough set method, with the aim of enhancing both accuracy and interpretability. The proposed mathematical model addresses the challenge of generating accurate and interpretable fuzzy rule sets, particularly in the context of credit risk assessment. By utilizing grid partitioning, the input space is divided into regions, while the rough set method is employed to identify relevant features. The results show improved accuracy in classifying loan applicants into low-risk and high-risk categories, accompanied by enhanced interpretability through the generation of clear and understandable rules. The model's applicability extends to credit risk assessment and offers potential for further refinement and research. However, it is crucial to consider certain limitations, including the generalizability of results, sensitivity to grid partitioning, and the trade-off between accuracy and interpretability. In conclusion, the proposed model exhibits promise in generating accurate and interpretable fuzzy rule sets, thereby contributing to effective decision-making processes across diverse domains.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123281107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Grid Partition and Rough Set Method for Generation of Fuzzy Rules in Dataset Classification 数据集分类中模糊规则生成的混合网格划分和粗糙集方法

International Journal of Enterprise Modelling Pub Date : 2018-12-30 DOI: 10.35335/emod.v13i1.3

Park Françoisee Vernadate

{"title":"Hybrid Grid Partition and Rough Set Method for Generation of Fuzzy Rules in Dataset Classification","authors":"Park Françoisee Vernadate","doi":"10.35335/emod.v13i1.3","DOIUrl":"https://doi.org/10.35335/emod.v13i1.3","url":null,"abstract":"The Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification is a novel approach aimed at addressing the challenges of classifying datasets with uncertainty and imprecision. This methodology combines the concepts of grid partitioning, rough set theory, and fuzzy rule generation to enhance classification accuracy and interpretability. The hybrid grid partitioning technique divides the attribute space into a grid structure, capturing the underlying structure and relationships in the dataset. Rough set theory is then utilized to analyze the dataset and identify relevant attributes, reducing dimensionality and improving classification efficiency. Fuzzy rule generation employs fuzzy logic to capture imprecise and uncertain knowledge present in the dataset, generating flexible and robust fuzzy rules. Rule evaluation and selection processes are employed to identify high-quality rules for accurate and interpretable classification models. The proposed methodology offers a comprehensive framework for handling complex datasets, demonstrating improved classification performance in various domains. Experimental evaluations and comparisons with other classification approaches validate the effectiveness and practicality of the Hybrid Grid Partition and Rough Set Method for Fuzzy Rule Generation in Dataset Classification. This research contributes to advancing the field of dataset classification, particularly in scenarios where uncertainty and imprecision are prevalent. The proposed approach offers a comprehensive framework for handling complex datasets and improving classification performance in various domains.","PeriodicalId":262913,"journal":{"name":"International Journal of Enterprise Modelling","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130845274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1