ナガタ　キヨシ   永田　清   経営学部 経営学科   教授

■ 標題   Predition Model with Interval Data -Toward Practical Applications-

■ 概要   The regression model is one of typical model for predicting some values by analyzing existing numerical data collected in various ways. If data are not crisp numbers, they are usually transformed into numerical crisp values by means of some methods such as quantification method. In companies' decision making process, collected and referred data usually have uncertainty which sometimes play an important roles for business performance. In this paper, we review some method for this purpose, then describe the model by applying it to some test cases.   Michihiro Amagasa, Kiyoshi Nagata   共著   Information Processing and Management of Uncertainty in Knoledge-Based Systems   Springer International Publishing Switzerland   (2),pp.213-224   2016/07

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