This document was presented on 8 May 2018 at the doctoral symposium at IFIP International Conference on Computational Intelligence and Its Applications (IFIP CIIA 2018) and got the price for first best presentation.

1. Randomization Approach in Case-
Based Reasoning: case of study of
mammography mass
Student:
Miled Basma BENTAIBA-LAGRID
2nd year LMD doctorate
Supervisors:
• Prof. Thouraya Bouabana-Tebibel
• Prof. Stuart H. Rubin
• Mrs. Lydia Bouzar-Benlabiod
LCSILaboratoire de Conception
de Systèmes
Informatiques

2. Agenda
• Definitions
• Subject & Motivation
• Proposed Approach
• Experiments
• Conclusion, Progress & FutureWork
• References
2

3. Definitions
Case1: problem1  solution1
Case2: problem2  solution2
…
Casen: problemn  solutionn
Case-base
Case: problem  solution
Case • First defined by (G. Chaitin
1975)
• Randomization means that
information or knowledge can
be effectively compressed
until that representation of
the compressed information is
random; or in other words,
pattern-less.
Randomization
Case-base
Case
problem
Retrieve
Reuse
Similar
cases
New case
Revise
Confirmed
case
Retain
.
Case-based-reasoning
3

4. Subject & Motivation
How to ensure
accuracy and
efficiency of CBR’s
problem resolution?
Current Solutions
• Feed the case-base using
inference methods
Problem
• A massive case-base
may deteriorate the
CBR’s rapidity of search
Proposed Solution
• Amplify the knowledge
using randomization,
which is a new approach
for data compression
Problem
• The generated cases
may not be valid
Solution
Validate the
generated cases
before their use
4

5. Proposed Approach (1)
Case-Based Reasoning System
Retrieve
Reuse
Revise
Retain
Segmented
case-base
Knowledge amplification
using randomization
Validation Module
Cases coherence verification
Cases stochastic validation
Cases absolute validation
coherent
cases
stochastic validity
> validity threshold
store the case new iteration
stochastic validity <
validity threshold
Rules Generation Module
rules-base
Rules generation using
randomization
Rules stochastic validation
Rules expert validation
using valid cases
5

6. Experiments
• Experiments are done to obtain the severity of mammography mass
Resolved problem rates Resolution time progress 6

7. Conclusion, Progress & Future Work
How rules are
(1) Generated?  Randomization technique for validation
(2) Maintained?  rule-base
(3) validated?  rules stochastic validation
The proposed segmentation of the case-base helps the randomization process to be
fulfilled. How the segmented case-base is used by the CBR?
 Define how Retrieve, Reuse, Revise & Retain are implemented.
Main contributions are:
(1) Randomization technique for amplification,
(2) Segmentation of the case-base,
(3)Validation prosses based on three layers,
(4) Identify the severity of a mammography mass
7

8. References
• Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues,
methodological variations, and system approaches. AI
Communications 7(1) 39-59 (1994)
• Chaitin, G.J.: Randomness and mathematical proof, Sci. Amer. 232
47–52 (1975)
8

9. Thank you