where nij is the number of examples from class i with attribute value Vj and nj is the number of examples with attribute value Vj . Consider the training set for the loan classification problem shown in Figure 5.9. Use the MVDM measure to compute the distance between every pair of attribute values for the Home Owner and Marital Status attributes.

The nearest-neighbor algorithm described in Section 5.2 can be extended to

handle nominal attributes. A variant of the algorithm called PEBLS (Parallel

Examplar-Based Learning System) by Cost and Salzberg [2] measures the

distance between two values of a nominal attribute using the modified value

difference metric (MVDM). Given a pair of nominal attribute values, V1 and

V2, the distance between them is defined as follows:

The training set shown in Figure 5.9 can be summarized for the Home Owner

and Marital Status attributes as follows.



d(Single, Married) = 1

d(Single, Divorced) = 0

d(Married, Divorced) = 1

d(Refund=Yes, Refund=No) = 6/7