Case Based Reasoning

# Case-Based Reasoning

Case-Based Reasoning uses the first two principles of Instance Based Learning:

1. Lazy Learning
2. Classify new instances based on those similar to them

But not the third
3. CBR doesn't represent instances as real-valued points in an n-dimensional Euclidean space.

In CBR, instances are typically represented using more rich symbolic descriptions, and the methods used to retrieve similar instances are correspondingly more elaborate.

For instance, we might represent an instance as the following (learner to create new mechanical designs):

As such they're more 'cases' than instances - quite different from each other, but with the same purpose.

# CBR Cycle

• RETRIEVE the most similar case or cases
• REUSE the information and knowledge useful to solve the problem from the case
• REVISE the proposed solution
• RETAIN the parts of this experience likely to be useful for future problem solving

# Similarity

The similarity metric different for Cases is quite different than the Euclidean distance for instances, due to their rich symbolic nature.

For instance we can use a match and mismatch function (counting the number of variables that match or don't as a proportion) to come up with something like:

(1)
\begin{align} score(Q,C) = \frac{match(Q,C) - mismatch(Q,C)}{|C|} \end{align}