A neuro-fuzzy inference system for stakeholder classification
ABSTRACT Stakeholder classification is carried out manually using methods such as brainstorming, interviews with experts, and checklists. These methods present a subjective character as they depend on the appreciation of the interviewees. This characteristic affects the accuracy of this classification, making that the project managers do not make the correct decisions. The research aims to suggest a fuzzy inference system for the classification of stakeholders, which will improve the quality of such classification in the projects. The proposal carries out the machine learning and the adjustment of the fuzzy inference system to classify the stakeholders by executing four algorithms based on artificial neural networks: ANFIS, HYFIS, FS.HGD, and FIR.DM. It analyzes the results of applying them in 10 iterations by calculating the measures: percentage of correct classifications, false-positive cases, false-negative cases, and mean square error. The ANFIS system show the best results. The fuzzy inference system for stakeholder classification generated improves the quality of this classification using machine learning, allowing to make better decisions in a project.
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Universidad de Tarapacá.
2022
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oai:scielo:S0718-330520220002003782022-08-25A neuro-fuzzy inference system for stakeholder classificationPérez Vera,YasielBermudez Peña,Anié Artificial neural networks fuzzy inference system project management stakeholder classification ABSTRACT Stakeholder classification is carried out manually using methods such as brainstorming, interviews with experts, and checklists. These methods present a subjective character as they depend on the appreciation of the interviewees. This characteristic affects the accuracy of this classification, making that the project managers do not make the correct decisions. The research aims to suggest a fuzzy inference system for the classification of stakeholders, which will improve the quality of such classification in the projects. The proposal carries out the machine learning and the adjustment of the fuzzy inference system to classify the stakeholders by executing four algorithms based on artificial neural networks: ANFIS, HYFIS, FS.HGD, and FIR.DM. It analyzes the results of applying them in 10 iterations by calculating the measures: percentage of correct classifications, false-positive cases, false-negative cases, and mean square error. The ANFIS system show the best results. The fuzzy inference system for stakeholder classification generated improves the quality of this classification using machine learning, allowing to make better decisions in a project.info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.30 n.2 20222022-06-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052022000200378en10.4067/S0718-33052022000200378 |
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Pérez Vera,Yasiel Bermudez Peña,Anié |
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Pérez Vera,Yasiel Bermudez Peña,Anié A neuro-fuzzy inference system for stakeholder classification |
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Pérez Vera,Yasiel Bermudez Peña,Anié |
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Pérez Vera,Yasiel |
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A neuro-fuzzy inference system for stakeholder classification |
title_short |
A neuro-fuzzy inference system for stakeholder classification |
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A neuro-fuzzy inference system for stakeholder classification |
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A neuro-fuzzy inference system for stakeholder classification |
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A neuro-fuzzy inference system for stakeholder classification |
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neuro-fuzzy inference system for stakeholder classification |
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ABSTRACT Stakeholder classification is carried out manually using methods such as brainstorming, interviews with experts, and checklists. These methods present a subjective character as they depend on the appreciation of the interviewees. This characteristic affects the accuracy of this classification, making that the project managers do not make the correct decisions. The research aims to suggest a fuzzy inference system for the classification of stakeholders, which will improve the quality of such classification in the projects. The proposal carries out the machine learning and the adjustment of the fuzzy inference system to classify the stakeholders by executing four algorithms based on artificial neural networks: ANFIS, HYFIS, FS.HGD, and FIR.DM. It analyzes the results of applying them in 10 iterations by calculating the measures: percentage of correct classifications, false-positive cases, false-negative cases, and mean square error. The ANFIS system show the best results. The fuzzy inference system for stakeholder classification generated improves the quality of this classification using machine learning, allowing to make better decisions in a project. |
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Universidad de Tarapacá. |
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2022 |
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http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052022000200378 |
work_keys_str_mv |
AT perezverayasiel aneurofuzzyinferencesystemforstakeholderclassification AT bermudezpenaanie aneurofuzzyinferencesystemforstakeholderclassification AT perezverayasiel neurofuzzyinferencesystemforstakeholderclassification AT bermudezpenaanie neurofuzzyinferencesystemforstakeholderclassification |
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