Feature's types and their role in differentiating classes for estimation of not fully described object

Authors are developing precedent approach to solving the problem of optimal decision making. The method they develop makes it possible to make the most adequate precedent selection in conditions where the object under consideration is not fully described, and cannot be estimated unambiguously. The originality of the approach offered by authors is in its focus on functioning with varying set of features (attributes). It is important for different applications, but it is especially important while supporting physician's decision making, who often has a lack of time and resources. The method presumes the need in differentiating possible object membership that may be done by widening of its feature space. This task may in its turn be reduced to investigating of feature's roles and their combinations (as in differential diagnosis and semiotics in medicine). In order to determine in what way should one retrieve missing features the authors offer to use the following conceptions: range, persistent feature combination, frequency of occurrence, availability of a feature, and object category.