Assessment of uncertainty in the projective tree test using an ANFIS learning approach

Authors:
Luis G. Martínez;Juan R. Castro;Guillermo Licea;Antonio Rodríguez-Díaz
Affiliations:
Universidad Autónoma de Baja California, Tijuana, México;Universidad Autónoma de Baja California, Tijuana, México;Universidad Autónoma de Baja California, Tijuana, México;Universidad Autónoma de Baja California, Tijuana, México
Venue:
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Year:
2011

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Abstract

In psychology projective tests are interpretative and subjective obtaining results based on the eye of the beholder, they are widely used because they yield rich and unique data and are very useful. Because measurement of drawing attributes have a degree of uncertainty it is possible to explore a fuzzy model approach to better assess interpretative results. This paper presents a study of the tree projective test applied in software development teams as part of RAMSET's (Role Assignment Methodology for Software Engineering Teams) methodology to assign specific roles to work in the team; using a Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) and also training data applying an ANFIS model to our case studies we have obtained an application that can help in role assignment decision process recommending best suited roles for performance in software engineering teams.