randomization approach in case-based reasoning: case of study of mammography mass
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A new way to amplify the case-based reasoning (CBR) knowledge-base using randomization. This method allows knowledge amplification without deteriorating the CBR's resolution time and it was applied to find the severity of mammography mass for patients.
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randomization approach in case-based reasoning: case of study of mammography mass
- 1. Randomization Approach in Case- Based Reasoning: case of study of mammography mass Authors: • Miled Basma BENTAIBA-LAGRID (LCSI, ESI) • Dr. Lydia Bouzar-Benlabiod (LCSI, ESI) • Prof. Stuart H. Rubin (SPAWAR) • Prof. Thouraya Bouabana-Tebibel (LCSI, ESI) • Maria Roumeissa Hanini (LCSI, ESI) 1
- 2. 2 Introduction The concept of problem solving for humans The case-based reasoning imitates the human thinking An evolutionary case- base versus a static case-base
- 3. 3 Definitions Randomization First defined by Chaitin 1975 Knowledge amplification Using transformation Knowledge induction Domain-specific Self-referenced
- 4. 4 Definitions Case / Case-Base • { Problem } solutionCase •Form of the case •Example 𝐶𝑗: 𝑡1: 𝑣𝑗,1, 𝑡2: 𝑣𝑗,2, … , 𝑡 𝑛: 𝑣𝑗,n → 𝑆𝑗 𝐶1: {𝐵𝐼 − 𝑅𝐴𝐷𝑆: 5, 𝑎𝑔𝑒: 67, 𝑠ℎ𝑎𝑝𝑒: 𝑙𝑜𝑏𝑢𝑙𝑎𝑟, 𝑚𝑎𝑟𝑔𝑖𝑛: 𝑠𝑝𝑒𝑐𝑢𝑙𝑎𝑡𝑒𝑑, 𝑑𝑒𝑛𝑠𝑖𝑡𝑦: 𝑙𝑜𝑤} → 𝑚𝑎𝑙𝑖𝑔𝑛𝑎𝑛𝑡
- 5. How to increase 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 Problem • The generated cases may not be valid Solution Validate the generated cases before their use 5 Motivation
- 6. • A generic method was proposed (S. H. Rubin) 6 Traveling Robots Scheduling systems Refrigerator design Cyber context Military strategies Related Work Randomization
- 7. Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case- base Knowledge amplification using randomization store the case new iteration Validation Module coherence verification stochastic validation absolute validation coherent cases stochastic validity > validity threshold stochastic validity < validity threshold Proposed Approach Global Overview 7
- 8. Proposed Approach The segmented Case-Base Similarities • Solution part similarities • Problem part similarities • Depends on the values of the attributes • Qualitative attributes • Quantitative attributes Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation 8
- 9. Proposed Approach The segmented Case-Base Attributes Weighting Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation 9 • Highly similar • Same solution V • Highly similar • Different solution X • Slightly similar • Same solution Y • Slightly similar • Different solution ZW = (V + Z) – (X + Y)
- 10. Proposed Approach The segmented Case-Base Knowledge Structuring Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation P highly similar to the delegate … … … P less highly similar to the delegate … P slightly similar to the delegate … … … … … … … … … delegate P1 … delegate Pk … delegate Pj Levels level1level2…leveln Sector represented by S = X1 C: problem (P) solution (S) Segmentrepresentedby: S=X1 Delegate=Pk Sector represented by with S = Xm Case form: 10
- 11. Proposed Approach Knowledge Amplification Using Randomization Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation Ca : {t1: va,1, t2: va,2, …, tj: va,j, tj+1: va,j+1, …, tn: va,n} S Cb : {t1: vb,1, t2: vb,2, …, tj: vb,j, tj+1: vb,j+1, …, tn: vb,n} S Let W1 > W2 > … Wj > Wj+1 > … > Wn be the weights of the attributes t1, t2, …, tj, tj+1, …, tn • Cc : {t1: vb,1, t2: vb,2, …, tj: vb,j, tj+1: va,j+1, …, tn: va,n} S • Cd : {t1: va,1, t2: va,2, …, tj: va,j, tj+1: vb,j+1, …, tn: vb,n} S 11
- 12. Proposed Approach Verification and Validation Three Steps Validation Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation coherent cases stochastic validity > validity threshold store the case new iteration stochastic validity < validity threshold 12
- 13. Proposed Approach Verification and Validation Coherence Verification Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation • Verification of the problem part • Verification of the solution part • Verification of the coherence of the case 13
- 14. Proposed Approach Verification and Validation Stochastic Validation Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation Parameters to calculate the stochastic validity: • The ratios of the frequency of the case by the frequency of the problem part regardless of the solution • How much is it similar to a fully valid case? Are the solutions the same or not? 14
- 15. Proposed Approach Verification and Validation Absolute Validation Case-Based Reasoning Retrieve Reuse Revise Retain Segmented case-base Knowledge amplification using randomization Validation Module coherence verification stochastic validation absolute validation • Cases that have a high stochastic validity are verified using absolute validation. • The absolute validation is fulfilled by the expert. 15
- 16. Application Domain Mammography Mass Attributes BI-RADS • From 1 and 5 Age • From 0 to 100 Shape • round • oval • lobular • irregular. Margin • circumscribed • micro-lobulate • obscured • ill-defined • speculated Density • high • iso • low • fat- containing. Severity • benign • malignant 16
- 17. Application Domain Mammography Mass Formalization / Data • UCI Machine Learning Repository (961 case, 688 non- duplicated) Data •Form of the case •Example 𝐵𝐼, 𝑎𝑔𝑒, 𝑠ℎ𝑎𝑝𝑒, 𝑚𝑎𝑟𝑔𝑖𝑛, 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 → {𝑠𝑒𝑣𝑒𝑟𝑖𝑡𝑦} 1 : :5, :67, : , : , : { } C BI RADS age shape lobular margin speculated density low malignant - ® 17
- 18. Experiments Randomization and the Resolution Accuracy1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 0 2 4 6 8 10 12 14 16 18 Experiments on 100 problems Numberofresolvedproblems Resolved Problems Rate 18
- 19. Experiments Randomization and the Resolution Time Resolution time progress 19
- 20. Conclusion 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 20
- 21. Future Work Improve the absolute validation using an automatic process How the CBR can use the segmented CB? 21
- 22. Thank you 22
- 23. Randomization Approach in Case- Based Reasoning: case of study of mammography mass Authors: • Miled Basma BENTAIBA-LAGRID bm_bentaiba@esi.dz • Dr. Lydia Bouzar-Benlabiod l_bouzar@esi.dz • Prof. Stuart H. Rubin stuart.rubin@navy.mil • Prof. Thouraya Bouabana-Tebibel t_tebibel@esi.dz • Maria Roumeissa Hanini m_hanini@esi.dz 23
Editor's Notes
- Humans can solve problems that they confront on daily basis. One of the methods of doing that is using previous experiences and adapt their solutions to the newly encountered problems. CBR imitates the human thinking. It has a case-base where a case is referred to an experience captured from the real word. It is clear that when we have a static and non-evolving case-base the problem resolution process won’t be efficient. On the other hand, a massive case-base may slow down the resolution process. This is the aim of using randomization process to amplify the case-base without affecting the resolution time. Don’t worry, I will explain what the randomization is in next slides.
- First defined by (G. Chaitin 1975) it 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 was first used for knowledge amplification purposes by professor Tebibel. The randomization allows us to amplify the knowledge, and also keeping the case-base optimized.
- Randomization is domain specific A generic method was proposed (S. H. Rubin) The formalization of the problem may vary from a domain to another Randomization has been used for different purposes, not only for knowledge amplification. In our work, we are focusing on knowledge amplification using randomization
- Solution part similarities Totally similar if they have the same class of solution/ not similar if they have different solution parts Problem part similarities Depends on the values of the attributes two types of attributes quantitative and qualitative
- Before doing the process of randomization we must define what are the most important attributes that can change the solution if it will be a small change in it’s value. So for each attribute, we compare each cases with each other and calculate the number of pairs where the solution is the same, and If the attributes are highly similar and have the same solution, maybe it’s the attribute that made that solution happen. Similarly, if the attributes are slightly similar and have a different solution maybe it’s because of this attribute that made the solution different. The opposite for X, even if the attributes are similar but the cases have a different solution so the attribute doesn’t have a big impact on the solution
- Let have two cases Ca and Cb. The cases belong to the same the same level of a segment. If we consider that numbAttr = j then: Ca : {t1: va,1, t2: va,2, …, tj: va,j, tj+1: va,j+1, …, tn: va,n} S Cb : {t1: vb,1, t2: vb,2, …, tj: vb,j, tj+1: vb,j+1, …, tn: vb,n} S Let W1 > W2 > … Wj > Wj+1 > … > Wn be the weights of the attributes t1, t2, …, tj, tj+1, …, tn The transformation by interchanging the jst highest weighted attributes begets the two following newly generated cases: Cc : {t1: vb,1, t2: vb,2, …, tj: vb,j, tj+1: va,j+1, …, tn: va,n} S Cd : {t1: va,1, t2: va,2, …, tj: va,j, tj+1: vb,j+1, …, tn: vb,n} S
- Red: without randomization Yellow: with randomization without validation Green: with randomization and 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. How to improve absolute validation to be automatic or semi-automatic? rule generation As for future work, first, we are planning to propose a more detailed way to perform the absolute validation using a semi-automatic process that generates rules from both valid and invalid cases. The aim of the future work is to reduce expert’s intervention and to speed up the validation process. Then, we will focus on how the CBR retrieves, reuse, revise and retain modules are implemented to benefit from the proposed structure of the case-base.