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 shows 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.