Support vector machines in projects risk classification
International Journal of Development Research
Support vector machines in projects risk classification
Received 19th August, 2018; Received in revised form 08th September, 2018; Accepted 18th October, 2018; Published online 28th November, 2018
Copyright © 2018, Domingos M. R. Napolitano et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Projects are fundamental to organizations, but they are subject to the occurrence of risks, which can affect their success. Therefore, project managers seek to manage risks by demanding decisions about their treatment. A widely used tool in this process is the Probability and Impact Matrix (PIM) that is popular, but deficient. This paper aimed to answer the question "How to classify risks in projects using Support Vector Machines (SVM)?". To answer this question, SVMs were applied to process risks, previously classified using an PIM. The results show that the SVM application returns more accurate results than the PIMs.
