1. Similarity in CBR Sources: –Chapter 4 –www.iiia.csic.es/People/enric/AICom.html –www.ai-cbr.org

2. Computing Similarity Similarity is a key (the key?) concept in CBR  We saw that a case consists of:  We saw that the CBR problem solving cycle consists of: similarity  Problem  Solution  Adequacy  Retrieval  Reuse  Revise  Retain similarity We will distinguish between:  Meaning of similarity  Formal axioms capturing this meaning

3. Meaning of Similarity Observation 1: Similarity always concentrates on one aspect or task:  There is no absolute similarity  Example: Two cars are similar if they have similar capacity (two compact cars may be similar to each other but not to a full-size car) Two cars are similar if they have similar price (a new compact car may be similar to an old full-size car but not to an old compact car)  When computing similarity we are concentrating on one such aspect or aggregating several such aspects

4. Meaning of Similarity (2) Observation 2: Similarity is not always transitive:  Example: I define similar to mean “within walking distance” “Lehigh’s book store” is similar to “Lupita” “Lupitas” is similar to “Perkins” “Perkins” is similar to “Monrovia book store” … But: “Lehigh’s book store” is not similar to “Best Buy” in Allentown !  The problem is that the property “small difference” cannot be propagated

5. Meaning of Similarity (3) Observation 3: Similarity is not always symmetric:  Example:  The problem is that in general the distance from an element to a prototype of a category is larger than the other way around “Mike Tyson fights like a lion” But do we really want to say that “a lion fights like Mike Tyson”?

6. Similarity and Utility in CBR Utility: measure of the improvement in efficiency as a result of a body of knowledge (We’ll come back to this point)  The goal of the similarity is to select cases that can be easily adapted to solve a new problem Similarity = Prediction of the utility of the case However:  The similarity is an a priori criterion  The utility is an a posteriori criterion Ideal: Similarity makes a good prediction of the utility

7. Axioms for Similarity There are 3 types of axioms:  Binary similarity predicate “x and y are similar”  Binary dissimilarity predicate “x and y are dissimilar”  Similarity as order relation: “x is at least as similar to y as it is to z” Observation:  The first and the second are equivalent  The third provides more information: grade of similarity

8. Similarity Relations We want to define a relation: R(x,y,z) iff “x is at least as similar to y as x is similar is to z” First lets consider the following relation: S(x,y,u,v) iff “x is at least as similar to y as u is similar to v”  Definition of R in terms of S: R(x,y,z) iff S(x,y,x,z)

9. Similarity Relations (2) Possible requirements on the relation S: 1.Reflexive: S(x,x,u,v) 2.Symmetry: S(x,y,y,x) 3.Transitivity: S(x,y,u,v) & S(u,v,s,t)  S(x,y,s,t) 4.Symmetry: S(x,y,u,v) iff S(y,x,u,v) iff S (x,y,v,u)

10. Similarity Relations (3)  In CBR we have an object x fixed when computing similarity. Which x? The new problem  We are looking for a y such that y is the most similar to x. In terms of R this be seen as:  z: R(x,y,z) Given a problem x we can define an ordering relation  x as follows :  y  x z iff R(x,y,z)  y > x z iff (y  x z and ¬ z  x y)  y ~ x z iff (y  x z and z  x y)

11. Similarity Metric We want to assign a number to indicate the similarity between a case and a problem Definition: A similarity metric over a set M is a function: sim: M  M  [0,1] Such that:  For all x in M: sim(x,x) = 1 holds  For all x, y in M: sim(x,y) = sim(y,x) “ the closer the value of sim(x,y) to 1, the more similar is x to y”

12. Similarity Metric (2)  Given a similarity metric: sim: M  M  [0,1], it induces a similarity relation S sim (x,y,u,v) and  x as follows: sim(x,y)  sim(u,v) sim(x,y)  sim(x,z) sim provides a quantitative value for similarity: 01 y1y1 y2y2 y3y3 y4y4 sim(x, y i ) Thus y 4 is more similar to x  For all x, y, u, v: S sim (x,y,u,v) holds if  For all x, y, z: y  x z if

13. Distance Metric Definition: A distance function over a set M is a function: d: M  M  [0,  ) Such that:  For all x in M: d(x,x) = 0 holds  For all x, y in M: d(x,y) = d(y,x) Definition: A distance function over a set M is a metric if:  For all x, y in M: d(x,y) = 0 holds then x = y  For all x, y, z in M: d(x,z) + d(z,y)  d(x,y)

14. Relation between Similarity and Distance Metric  Given a distance metric, d, it induces a similarity relation S d (x,y,u,v),  x as follows:  For all x, y, u, v: S(x,y,u,v) holds if  For all x, y, z: y  x z if Definition: A similarity metric sim and a distance metric d are compatible iff: for all x,y, u, v: S d (x,y,u,v) iff S sim (x,y,u,v) d(x,y)  d(u,v) d(x,y)  d(x,z)

15. Relation between Similarity and Distance Metric (2) Property: Let f: [0,  )  (0,1] Be a bijective and order inverting (if u< v then f(v) < f(u)) function such that: f(0) = 1 f(d(x,y)) = sim(x,y) then d and sim are compatible If d(x,y) sim(u,v) f(d(x,y)) > f(d(u,v))

16. Relation between Similarity and Distance Metric (3) F(x) can be used to construct sim giving d. Example of such a function is: if you have the Euclidean distance: d((x,y),(u,v)) = sqr((x-u) 2 + (y-v) 2 ) Since f(x) = 1 – (x/(x+1)) meets the property before Then: sim((x,y),(u,v))) = f(d((x,y),(u,v))) = 1 – (d((x,y),(u,v)) /(d((x,y),(u,v)) +1)) is a similarity metric

17. Relation between Similarity and Distance Metric (3) The function f(x) = 1 – (x/(x+1)) is a bijective function from [0,  ) into (0,1]: 0 1

18. Other Similarity Metrics Suppose that we have cases represented as attribute-value pairs (e.g., the restaurant domain) Suppose initially that the values are binary We want to define similarity between two cases of the form: X = (X 1, …, X n ) where X i = 0 or 1 Y = (Y 1, …,Y n ) where Y i = 0 or 1

19. Preliminaries Let:  A =  (i=1,n) X i Y i  B =  (i=1,n) X i (1-Y i )  C =  (i=1,n) (1-X i )Y i  D =  (i=1,n) (1-X i ) (1-Y i )  Then, A + B + C + D = (number of attributes for which X i =1 and Y i = 1) (number of attributes for which X i =1 and Y i = 0) (number of attributes for which X i =0 and Y i = 1) (number of attributes for which X i =0 and Y i = 0) n A+D = B+C= “matching attributes” “mismatching attributes”

20. Hamming Distance H(X,Y) = n –  (i=1,n) X i Y i –  (i=1,n) (1-X i )(1-Y i ) Properties:  Range of H:  H counts the mismatch between the attribute values  H is a distance metric:  H((1-X 1, …, 1-X n ), (1-Y 1, …,1-Y n )) = [0,n] H(X,X) = 0 H(X,Y) = H(Y,X) H((X 1, …, X n ), (Y 1, …,Y n ))

21. Simple-Matching-Coefficient (SMC)  H(X,Y) = n – (A + D) = B + C Another distance-similarity compatible function is f(x) = 1 – x/max (where max is the maximum value for x)  We can define the SMC similarity, sim H : sim H (X,Y) = 1 – ((n – (A+D))/n) = (A+D)/n = 1- ((B+C)/n) Proportion of the difference # of mismatches

22. Simple-Matching-Coefficient (SMC) (II) If we use on sim H (X,Y) = (A+D)/n =1- ((B+C)/n) = factor(A, B, C, D)  Monotonic:  If A  A’ then:  If B  B’ then:  If C  C’ then:  If D  D’ then: factor(A,B,C,D)  factor(A’,B,C,D) factor(A,B’,C,D)  factor(A,B,C,D) factor(A,B,C’,D)  factor(A,B,C,D) factor(A,B,C,D)  factor(A,B,C,D’)  Symmetric: sim H (X,Y) = sim H (Y,X)

23. Variations of the SMC The hamming similarity assign equal value to matches (both 0 or both 1) There are situations in which you want to count different when both match with 1 as when both match with 0  Thus, sim((1-X 1, …, 1-X n ), (1-Y 1, …,1-Y n )) = sim((X 1, …, X n ), (Y 1, …,Y n )) may not hold  Example: Two symptoms of patients are similar if they both have fever (X i = 1 and Y i = 1) but not similar if neither have fever (X i = 0 and Y i = 0)  Specific attributes may be more important than other attributes Example: manufacturing domain: some parts of the workpiece are more important than others

24. Variations of SMC (III) We introduce a weight, , with 0 <  < 1: sim H (X,Y) = (A+D)/n = (A+D)/(A+B+C+D) sim  (X,Y) = (  (A+D))/ (  (A+D) + (1 -  )(B+C))  For which  is sim  (X,Y) = sim H (X,Y)?  = 0.5  sim  (X,Y) preserves the monotonic and symmetric conditions

25. The similarity depends only from A, B, C and D (3) What is the role of  ? What happens if  > 0.5? If  < 0.5? sim  (X,Y) = (  (A+D))/ (  (A+D) + (1 -  )(B+C)) 1 0 0 n  = 0.5  > 0.5  < 0.5 If  > 0.5 we give more weights to the matching attributes If  < 0.5 we give more weights to the miss- matching attributes

26. Discarding 0-match Thus, sim((1-X 1, …, 1-X n ), (1-Y 1, …,1-Y n )) = sim((X 1, …, X n ), (Y 1, …,Y n )) may not hold Only when the attribute occurs (i.e., X i = 1 and Y i = 1 ) will contribute to the similarity  Possible definition of the similarity: sim = A / (A+ B+C)

27. Specific Attributes may be More Important Than Other Attributes Significance of the attributes varies Weighted Hamming distance: H W (X,Y) = 1 –  (i=1,n)  i X i Y i –  (i=1,n)  i (1-X i )(1-Y i )  There is a weight vector: (  1, …,  n ) such that  (i=1,n)  i = 1 Example: “Process planning: some features are more important than others”

28. Non Monotonic Similarity The monotony condition in similarity, formally, says that: sim(A,B)  sim(A’,B) always holds if A counts the number of matches and A  A’ Informally the monotony condition can be expressed as: For any X, Y, X’ attribute-value vectors, If we obtain X’ by modifying X on the value of one attribute such that X’ and Y have the same value on that attribute then: sim(X,Y) sim(X’,Y) 

29. Non Monotonic Similarity (2) sim H (X,Y) =  (i=1,n) eq(X i,Y i ) / n  Is the hamming distance monotonic? Yes  Consider the XOR function:  (0,0) and (1,1) are on the same class (+)  (0,1) and (1,0) are on the same class (-)  Thus d((1,1),(1,0)) > d((1,1),(0,0))  Is this monotonic? No

30. Non Monotonic Similarity (3) You may think: “well that was mathematics, how about real world?” Suppose that we have two interconnected batteries B and B’ and 3 lamps X, Y and Z that have the following properties:  If X is on, B and B’ work  If Y is on, B or B’ work  If Z is on, B works 1 0 1 1 Ok Fail 2 0 1 0 Fail Ok 3 0 0 0 Fail Fail Situation X Y Z B B’ Thus: sim(1,3) > sim(1,2) Non monotonic!

31. Tversky Contrast Model Defines a non monotonic distance Comparison of a situation S with a prototype P (i.e, a case) S and P are sets of features The following sets:  A = S  P  B = P – S  C = S – P A S P C B

32. Tversky Contrast Model (2) Tversky-distance: Where f:  [0,  ) f, , , and  are fixed and defined by the user Example:  If f(A) = # elements in A   =  =  = 1  T counts the number of elements in common minus the differences  The Tversky-distance is not symmetric T(P,S) =  f(A) -  f(B) -  f(C)

33. Local versus Global Similarity Metrics In many situations we have similarity metrics between attributes of the same type (called local similarity metrics). Example: For a complex engine, we may have a similarity for the temperature of the engine In such situations a reasonable approach to define a global similarity sim  (x,y) is to “aggregate” the local similarity metrics sim i (x i,y i ). A widely used practice sim  (x,y) to increate monotonically with each sim i (x i,y i ). What requirements should we give to sim  (x,y) in terms of the use of sim i (x i,y i )?

34. Local versus Global Similarity Metrics (Formal Definitions) A local similarity metric on an attribute T i is a similarity metric sim i : T i  T i  [0,1] A function  : [0,1] n  [0,1] is an aggregation function if:   (0,0,…,0) = 0   is monotonic non-decreasing on every argument Given a collection of n similarity metrics sim 1, …, sim n, for attributes taken values from T i, a global similarity metric, is a similarity metric sim:V  V  [0,1], V in T 1  …  T n, such that there is an aggregation  function with: sim(X,Y) = sim  (X,Y) =  (sim 1 (X 1,Y 1 ), …,sim n (X n,Y n ))  (X 1,X 2,…,X n ) = (X 1 +X 2 +…+X n )/n Example:

35. Example Cases may contain attributes of type: –real number A: the voltage output of a device define a local similarity metric, sim voltage () –Integer B: revolutions per second define a local similarity metric, sim rps () –A bunch of symbolic attributes  m = (C 1,..,C m ): front light blinking or none, year of manufacture, etc define a Hamming similarity, sim H (), combining all these attributes Define an aggregated similarity sim() metric: sim(C,C’) =  1 *sim voltage (A,A’) +  2 *sim voltage (A,A’) +  3 *sim H (  m,  m ’)

36. Homework (1 of 2) 1.In Slide 12 we define the similarity relation S sim (x,y,u,v). Which of the 4 kinds of relations defined in Slide 9 are satisfied by S sim (x,y,u,v)? 2.Let us define: S H (x,y,u,v) iff H(x,y)  H(u,v) where H is the Hamming distance (defined in Slide 20). Which of the 4 kinds of relations defined in Slide 9 are satisfied by S H (x,y,u,v)? 3. Let us define: S T (x,y,u,v) iff T(x,y)  T(u,v) where T is the Tversky Contrast Model (defined in Slide 31). Which of the 4 kinds of relations defined in Slide 9 are satisfied by S T (x,y,u,v)?

37. Homework (2 of 2) 4. X = (X 1, …, X n ) where X i  T i Y = (Y 1, …,Y n ) where Y i  T i Each T i is finite Define a formula for the Hamming distance when the attributes are symbolic but may take more than 2 values: