So we have potential for a machine learning application that fits fairly neatly into the traditional problem domain solved by classifiers, i.e., we have a set of attributes describing an item and a "bucket" that they end up in. However, rather than create models of probabilities like in Naive Bayes or similar classifiers, we want our output to be a set of roughly human-readable rules that can be reviewed and modified by an end user.
Association rule learning looks like the family of algorithms that solves this type of problem, but these algorithms seem to focus on identifying common combinations of features and don't include the concept of a final bucket that those features might point to. For example, our data set looks something like this:
Item A { 4-door, small, steel } => { sedan }
Item B { 2-door, big, steel } => { truck }
Item C { 2-door, small, steel } => { coupe }
I just want the rules that say "if it's big and a 2-door, it's a truck," not the rules that say "if it's a 4-door it's also small."
One workaround I can think of is to simply use association rule learning algorithms and ignore the rules that don't involve an end bucket, but that seems a bit hacky. Have I missed some family of algorithms out there? Or perhaps I'm approaching the problem incorrectly to begin with?
