This slide has two sections. The first section defines case based reasoning, and pros and cons. In the second section a case study which name is housing price is introduced.

1. AMIR BADAMCHI
CASE-BASED REASONING CASE STUDY: HOUSING PRICEAmirkabirUniversity of TechnologyComputer Engineering & Information Technology Faculty

2. CONTENTS
•CBR
•Definition
•Assumptions
•Cycle
•Advantages, disadvantages
•Housing Price
•Introduction
•Method
•Estimation Function
•Similarity Function
•Results

3. DEFINITION
•Case-based reasoning is […] reasoning by remembering -Leake, 1996
•A case-based reasonersolves new problems by adapting solutions that were used to solve old problems -Riesbeck& Schank, 1989
•Case-based reasoning is a recent approach to problem solving and learning […] -Aamodt& Plaza, 1994

4. CBR ASSUMPTIONS
•The main assumption is that:
•Similar problems have similar solutions:
•e.g., an aspirin can be taken for any mild pain
•Two other assumptions:
•The world is a regular place:what holds true today will probably hold true tomorrow
•(e.g., if you have a headache, you take aspirin, because it has always helped)
•Situations repeat:if they do not, there is no point in remembering them
•(e.g., it helps to remember how you found a parking space near that restaurant)

5. CBR CYCLE
•Retrieve:
•Determine most similar case(s).
•Reuse:
•Solve the new problem re-using information and knowledge in the retrieved case(s).
•Revise:
•Evaluate the applicability of the proposed solution in the real-world.
•Retain:
•Update case base with new learned case for future problem solving

6. CBR CYCLE

7. ADVANTAGES
•solutions are quickly proposed
•derivation from scratch is avoided
•domains do not need to be completely understood
•cases useful for open-ended/ill-defined concepts
•highlights important features

8. DISADVANTAGES
•old cases may be poor
•library may be biased
•most appropriate cases may not be retrieved
•retrieval/adaptation knowledge still needed

9. HOUSING PRICE
•Mary wishes to sell her apartment in the city.
•She might start with the price she paid for her apartment and add an annual appreciation that seems reasonable to her.
•She might try to predict market trends and figure out how much the apartment should be worth.

10. HOUSING PRICE
•General rules
•In this area, the price per squared meter is $3,000..
•Case-based
•The apartment next door, practically identical to mine, was just sold for $300,000..

11. METHOD

12. METHOD(CONT)

13. ESTIMATION FUNCTION
•Use parametric approach
•Advantages:
•Simplify to analyse
•Comparasionof two models
•HyphotehesTest

14. SIMILARITY FUNCTION
•Weighted euclideandistance
•Why weighted euclideandistance instead standard euclideandistance
•Variables with differenetscales
•Variables with differenetinfluent
•Allow a wide range of distance functions, weighing the relative importance of variables

15. SIMILARITY FUNCTION
•Translate the distance function to a similarity function
•decreasing in the distance
•The distance goes up from 0 to
•The similarity function goes down from 1 (maximal similarity) to 0.

16. RESULTS
•Goodness of fit measures for regression and similarity, for the two databases.
LIKE :Value of the log-likelihood function (in-sample, 75% of the data points)
SSPE: Sum of Squared Prediction Errors (out of sample, remaining 25% of the data points)
AIC: AkaikeInformation Criterion (computed over the whole sample)
SC : Schwarz Criterion (computed over the whole sample)

17. REFERENCES
•Ian Watson, “An Introduction to Case-Based Reasoning”, 1995.
•Gayer, Gilboa, Lieberman,"Rule-Based and Case- Based Reasoning in Housing Prices", 2007.
•Billot, A., I. Gilboa, D. Samet, and D. Schmeidler, "Probabilities as Similarity-Weighted Frequencies", 2005.

18. Any question?

19. THAT’S ALL FOLKS!