1. Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system Expert Systems with Applications 28(2005) Author: R.J. Kuo, Y.P. Kuo, Kai-Ying Chen Speaker: Chih-Yao Chien

2. Outline Introduction CBR – Fuzzy CBR ACS – ASCA – Fuzzy ant K-means algorithm Experiment Q&A

3. Introduction In order to cope with huge amount of data and information in the business, varieties of methods including artificial intelligence and statistical methods are developed to extract valuable information from the raw data. Case-based reasoning is one of these methods.

4. Case-based reasoning (CBR) CBR - Searching for similar cases from the historical cases for user as consulting references in solving needed problems. CBR cycle: retrieve, reuse, revise, retain.

6. Case-based reasoning (CBR) Fuzzy CBR – fuzzy sets theory is used for evaluating similarity between a new case and the existing cases in the case base. In general CBR matching mechanism, successful matching of a selected index is an all-or-nothing affair.  the interval may be too small or specific resulting in no matches for a given observed feature set.  requiring a very large case library to cover the input space.

7. Case-based reasoning (CBR) Fuzzy similarity method is proposed to improve the effectiveness of indexing and matching accuracy.

9. Ant colony system (ACS)

10. Ant colony system (ACS)-ASCA Fuzzy ant system-based clustering algorithm

11. Ant colony system (ACS)-Fuzzy AK Fuzzy ant K-means algorithm

12. Experiment

14. Fuzzy sets theory indeed improves the ASCA+AK method. This study has presented the capability advantages of using fuzzy CBR. – Thinking style of human beings. – Easily extract the domain knowledge experts’ know-how. – Searching time is considerably less.

15. Experiment Drawbacks – Find sufficient amount of cases. – Most domain experts are not willing to provide their own know-how. – If there are too many selections for a single attribute, it is possible that these selections are extremely close.