  Purpose: This paper presents an algorithm that can elicitate (infer) all or
any combination of ELECTRE Tri-B parameters. For example, a decision-maker can
maintain the values for indifference, preference, and veto thresholds, and our
algorithm can find the criteria weights, reference profiles, and the lambda
cutting level. Our approach is inspired by a Machine Learning ensemble
technique, the Random Forest, and for that, we named our approach as ELECTRE
Tree algorithm. Methodology: First, we generate a set of ELECTRE Tri-B models,
where each model solves a random sample of criteria and alternatives. Each
sample is made with replacement, having at least two criteria and between 10%
to 25% of alternatives. Each model has its parameters optimized by a genetic
algorithm that can use an ordered cluster or an assignment example as a
reference to the optimization. Finally, after the optimization phase, two
procedures can be performed, the first one will merge all models, finding in
this way the elicitated parameters, and in the second procedure each
alternative is classified (voted) by each separated model, and the majority
vote decides the final class. Findings: We have noted that concerning the
voting procedure, non-linear decision boundaries are generated, and they can be
suitable in analyzing problems with the same nature. In contrast, the merged
model generates linear decision boundaries. Originality: The elicitation of
ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a
set of multicriteria models that are engaged in generating robust solutions.
