  As one type of machine-learning model, a "decision-tree ensemble model"
(DTEM) is represented by a set of decision trees. A DTEM is mainly known to be
valid for structured data; however, like other machine-learning models, it is
difficult to train so that it returns the correct output value for any input
value. Accordingly, when a DTEM is used in regard to a system that requires
reliability, it is important to comprehensively detect input values that lead
to malfunctions of a system (failures) during development and take appropriate
measures. One conceivable solution is to install an input filter that controls
the input to the DTEM, and to use separate software to process input values
that may lead to failures. To develop the input filter, it is necessary to
specify the filtering condition of the input value that leads to the
malfunction of the system. Given that necessity, in this paper, we propose a
method for formally verifying a DTEM and, according to the result of the
verification, if an input value leading to a failure is found, extracting the
range in which such an input value exists. The proposed method can
comprehensively extract the range in which the input value leading to the
failure exists; therefore, by creating an input filter based on that range, it
is possible to prevent the failure occurring in the system. In this paper, the
algorithm of the proposed method is described, and the results of a case study
using a dataset of house prices are presented. On the basis of those results,
the feasibility of the proposed method is demonstrated, and its scalability is
evaluated.
