Constructing X-of-N attributes with a genetic algorithm

The predictive accuracy obtained by a classification

algorithm is strongly dependent on the quality of the

attributes of the data being mined. When the attributes are

little relevant for predicting the class of a record, the

predictive accuracy will tend to be low. To combat this

problem, a natural approach consists of constructing new

attributes out of the original attributes. Many attribute

construction algorithms work by simply constructing

conjunctions and/or disjunctions of attribute-value pairs.

This kind of representation has a limited expressiveness

power to represent attribute interactions. A more

expressive representation is X-of-N [Zheng 1995]. An Xof-

N condition consists of a set of N attribute-value pairs.

The value of an X-of-N condition for a given example

(record) is the number of attribute-value pairs of the

example that match with the N attribute-value pairs of the

condition. For instance, consider the following X-of-N

condition: X-of-{“Sex = male”, “Age < 21”, “Salary =

high”}. Suppose that a given example has the following

attribute-value pairs: {“Sex = male”, “Age = 51”, “Salary

= high”}. This example has 2 out of the 3 attribute-value

pairs of the X-of-N condition, so that the value of the Xof-

N condition for this example is 2.