Class Discovery

See http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-417.pdf

CDGA uses a Genetic Algorithm to discover a classification rule for a given dataset. A dataset can be seen as a table:

An attribute can be numerical, for example a “temperature” attribute, or categorical, for example a “color” attribute. For classification purposes, one of the categorical attributes is designated as a label, which means that its value defines the class of the rows. A classification rule can be represented as follows:

For a given target class and a weight threshold, the classification rule can be read :

for each row of the dataset
  if (rule.w1 < threshold || (rule.w1 >= threshold && row.value1 rule.op1 rule.value1)) &&
     (rule.w2 < threshold || (rule.w2 >= threshold && row.value2 rule.op2 rule.value2)) &&
     ...
     (rule.wN < threshold || (rule.wN >= threshold && row.valueN rule.opN rule.valueN)) then
    row is part of the target class

Important: The label attribute is not evaluated by the rule.

The threshold parameter allows some conditions of the rule to be skipped if their weight is too small. The operators available depend on the attribute types:

• for a numerical attributes, the available operators are ‘<’ and ‘>=’
• for categorical attributes, the available operators are ‘!=’ and ‘==’

The “threshold” and “target” are user defined parameters, and because the label is always a categorical attribute, the target is the (zero based) index of the class label value in all the possible values of the label. For example, if the label attribute can have the following values (blue, brown, green), then a target of 1 means the “blue” class.

For example, we have the following dataset (the label attribute is “Eyes Color”):

This rule can be read as follows:

for each row of the dataset
  if (0 < 1 || (0 >= 1 && row.value1 < 20)) &&
     (1 < 1 || (1 >= 1 && row.value2 != light)) then
    row is part of the "brown Eye Color" class

Please note how the rule skipped the label attribute (Eye Color), and how the first condition is ignored because its weight is < threshold.

Running the example:

NOTE: Substitute in the appropriate version for the Mahout JOB jar

1. cd /examples
2. ant job
3. {code}/bin/hadoop dfs -put