Rough Set - Incomplete Data

Incomplete Data

Rough set theory is useful for rule induction from incomplete data sets. Using this approach we can distinguish between three types of missing attribute values: lost values (the values that were recorded but currently are unavailable), attribute-concept values (these missing attribute values may be replaced by any attribute value limited to the same concept), and "do not care" conditions (the original values were irrelevant). A concept (class) is a set of all objects classified (or diagnosed) the same way.

Two special data sets with missing attribute values were extensively studied: in the first case, all missing attribute values were lost (Stefanowski and Tsoukias, 2001), in the second case, all missing attribute values were "do not care" conditions (Kryszkiewicz, 1999).

In attribute-concept values interpretation of a missing attribute value, the missing attribute value may be replaced by any value of the attribute domain restricted to the concept to which the object with a missing attribute value belongs (Grzymala-Busse and Grzymala-Busse, 2007). For example, if for a patient the value of an attribute Temperature is missing, this patient is sick with flu, and all remaining patients sick with flu have values high or very-high for Temperature when using the interpretation of the missing attribute value as the attribute-concept value, we will replace the missing attribute value with high and very-high. Additionally, the characteristic relation, (see, e.g., Grzymala-Busse and Grzymala-Busse, 2007) enables to process data sets with all three kind of missing attribute values at the same time: lost, "do not care" conditions, and attribute-concept values.