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AACIMP 2011 Summer School. Operational Research Stream. Lecture by Erik Kropat.

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- 1. Summer School“Achievements and Applications of Contemporary Informatics, Mathematics and Physics” (AACIMP 2011) August 8-20, 2011, Kiev, Ukraine Classification Erik Kropat University of the Bundeswehr Munich Institute for Theoretical Computer Science, Mathematics and Operations Research Neubiberg, Germany
- 2. ExamplesClinical trialsIn a clinical trial 20 laboratory values of 10.000 patients are collected togetherwith the diagnosis ( ill / not ill ). We measure the values of a new patient. Is he / she ill or not?Credit ratingsAn online shop collects data from its customers together with some informationabout the credit rating ( good customer / bad customer ). We get the data of a new customer. Is he / she a good customer or not?
- 3. Machine-Learning /Classification New Example Labeled Machine Classification training learning rule examples algorithm Predicted classification
- 4. k Nearest Neighbor Classification ̶ kNN ̶
- 5. k Nearest Neighbor ClassificationIdea: Classify a new object with regard to a set of training examples. Compare the new object with the k “nearest” objects (“nearest neighbors”) ̶ ̶ ̶ + ̶ ̶ ̶ ̶ Objects in class 1 ̶ + + + + ̶ ̶ + ̶ + Objects in class 2 ̶ + ̶ ̶ ̶ ̶ ̶ ̶ New object ̶ ̶ + ̶ ̶ + ̶ ̶ + ̶ ̶ + ̶ 4-nearest neighbor
- 6. k Nearest Neighbor Classification New object • Required ̶ ̶ ̶ − Training set, i.e. objects and their class labels ̶ ̶ ̶ + ̶ ̶ + + − Distance measure + + ̶ ̶ + ̶ + ̶ − The number k of nearest neighbors ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ + ̶ ̶ + ̶ • Classification of a new object ̶ + ̶ ̶ + ̶ − Calculate the distance between the objects of the training set. 5-nearest neighbor − Identify the k nearest neighbors. − Use the class label of the k nearest neighbors to determine the class of the new object (e.g. by majority vote).
- 7. k Nearest Neighbor Classification 1-nearest neighbor 2-nearest neighbor 3-nearest neighbor ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ + ̶ + ̶ + ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ + ̶ + ̶ + ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ + ̶ ̶ + ̶ ̶ + ̶ + ̶ ̶ + ̶ ̶ + ̶Classification ̶ ? + Class label: Decision by distance ̶
- 8. 1-nearest neighbor ⇒ Voronoi diagram
- 9. kNN ̶ k Nearest Neighbor ClassificationDistance• The distance between the new object and the objects in the set of training samples is usually measured by the Euclidean metric or the squared Euclidean metric.• In text mining the Hamming-distance is often used.
- 10. kNN ̶ k Nearest Neighbor ClassificationClass label of the new object• The class label of the new object is determined by the list of the k nearest neighbors. This could be achieved by − Majority vote with regard to the class labels of the k nearest neighbors. − Distance of the k nearest neighbors.
- 11. kNN ̶ k Nearest Neighbor Classification• The value of k has a strong influence on the classification result. − k too small: Noise can have a strong influence. − k too large: Neighborhood can contain objects from different classes (ambiguity / false classification) + ̶ ̶ ̶ + ̶ ̶ ̶ ̶ ̶ ̶ + ̶ ̶ ̶ ̶ ̶ ̶ ̶ + ̶ ̶ ̶ + + ̶ + + ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ ̶ + ̶ ̶ ̶ ̶ + ̶ ̶
- 12. Support Vector Machines
- 13. Support Vector MachinesA set of training samples with objects in Rn is divided in two categories: positive objects and negative objects
- 14. Support Vector MachinesGoal: “Learn” a decision rule from the training samples. Assign a new example into the “positive” or the “negative” category.Idea: Determine a separating hyperplane. New objects are classified as positive, if they are in the half space of positive examples negative, if they are in the half space of negative examples.
- 15. Support Vector MachinesINPUT: Sample of training data Data from patients with confirmed T = { (x1, y1),...,(xk, yk) | xi ∈ Rn , yi ∈ { -1, +1 } }, diagnosis with xi ∈ Rn data Laboratory values and yi ∈ {-1, +1} class label Disease: Yes / NoDecision rule: INPUT: Laboratory values f : Rn → {-1, +1} of a new patient Decision: Disease: Yes / No
- 16. Separating HyperplaneA separating hyperplane is determined by − a normal vector w and H − a parameter b scalar product w H = { x ∈ Rn | 〈 w, x 〉 ̶ b = 0 }Offset of the hyperplane from the origin along w: b ____ ‖w ‖ Idea: Choose w and b, such that the hyperplane separates the set of training samples in an optimal way.
- 17. What is a good separating hyperplane?There exist many separating hyperplanes Will this new object be in the “red” class?
- 18. Question: What is the best separating hyperplane?Answer: Choose the separating hyperplane so that the distance from it to the nearest data point on each side is maximized. support vector maximum-margin hyperplane H margin support vector
- 19. Scaling of Hyperplanes• A hyperplane can be defined in many ways: For c ≠ 0: { x ∈ Rn | 〈 w, x 〉 + b = 0 } = { x ∈ Rn | 〈 cw, x 〉 + cb = 0 }• Use trainings samples to choose (w, b), such that Min | 〈 w, xi 〉 + b | = 1 xi canonical hyperplane
- 20. DefinitionA training sample T = {(x1, y1),...,(xk, yk) | xi ∈ Rn , yi ∈ {-1, +1} } is separableby the hyperplane H = { x ∈ Rn | 〈 w, x 〉 + b = 0 }, Hif there exists a vector w ∈ Rnand a parameter b ∈ R, such that w 〈 w, x 〉 + b = 1 〈 w, xi 〉 + b ≥ +1 , falls yi = +1 〈 w, xi 〉 + b ≤ ̶ 1 , falls yi = ̶ 1for all i ∈ {1,...,k}. 〈 w, x 〉 + b = -1
- 21. Maximal Margin H• The above conditions can be rewritten: w 〈 w, x 〉 + b = 1 yi · ( 〈 w, xi 〉 + b ) ≥ 1 for all i ∈ {1,...,k}• Distance between the two margin hyperplanes: 〈 w, x 〉 + b = -1 2 ____ ‖w ‖ ⇒ In order to maximize the margin we must minimize ‖ w ‖
- 22. Optimization problemFind a normal vector w and a parameter b, such that the distance betweenthe training samples and the hyperplane defined by w and b is maximized. Minimize __ ‖ w ‖ 2 1 H 2 s.t. yi · ( 〈 w, xi 〉 + b ) ≥ 1 for all i ∈ {1,...,k} w ⇒ quadratic programming problem
- 23. Dual Form Find parameters α1,...,αk, such that k k Kernel function Max Σ αi ̶ 1 Σ αα y y ̶ i j i j 〈 xi, xj 〉 i=1 2 i, j = 1 k ( xi, xj ) := 〈 xi, xj 〉 with αi ≥ 0 for all i = 1,...,k k Σ αi yi = 0 i=1 The maximal margin hyperplane (= the classification problem)⇒ is only a function of the support vectors.
- 24. Dual Form• When the optimal Parameters α*,...,α* are known, the normal vector w* 1 k of the separating hyperplane is given by k w* = Σ α* yi xi i training data i=1• The parameter b* is given by 1 max { 〈 w*, xi 〉 | yi = ̶ 1 } b* = _ _ + min { 〈 w*, xi 〉 | yi = +1 } 2
- 25. Classifier• A decision function f maps a new object x ∈ Rn to a category f(x) ∈ {-1, +1} : +1 , if 〈 w*, x 〉 + b* ≥ +1 f (x) = ̶ 1 , if 〈 w*, x 〉 + b* ≤ ̶ 1 H +1 w -1
- 26. Support Vector Machines ̶ Soft Margins ̶
- 27. Soft Margin Support Vector Machines• Until now: Hard margin SVMs The set of training samples can be separated by a hyperplane.• Problem: Some elements of the trainings samples can have a false label The set of training samples can not be separated by a hyperplane and SVM is not applicable.
- 28. Soft Margin Support Vector Machines• Idea: Soft margin SVMs Modified maximum margin method for mislabeled examples.• Choose a hyperplane that splits the training set as cleanly as possible, while still maximizing the distance to the nearest cleanly split examples.• Introduce slack variables ξ1,…, ξ n which measure the degree of misclassification.
- 29. Soft Margin Support Vector Machines• Interpretation The slack variables measure the degree of misclassification of the training examples with regard to a given hyperplane H. H ξi ξj
- 30. Soft Margin Support Vector Machines• Replace the constraints yi · ( 〈 w, xi 〉 + b ) ≥ 1 for all i ∈ {1,...,n} by yi · ( 〈 w, xi 〉 + b ) ≥ 1 ̶ ξ i for all i ∈ {1,...,n} H ξi
- 31. Soft Margin Support Vector Machines• Idea If the slack variables ξ i are small, then: ξi = 0 ⇔ xi is correctly classified H 0 < ξi < 1 ⇔ xi is between the margins. ξi ξi ≥ 1 ⇔ xi is misclassified [ yi · ( 〈 w, xi 〉 + b ) < 0 ] Constraint: yi · ( 〈 w, xi 〉 + b ) ≥ 1 ̶ ξ i for all i ∈ {1,...,n}
- 32. Soft Margin Support Vector Machines• The sum of all slack variables is an upper bound for the total training error: n Σ ξi i=1 H ξi ξj
- 33. Soft Margin Support Vector MachinesFind a hyperplane with maximal margin and minimal training error. regularisation * n __ ‖ w 1 ‖ Σ ξi 2 C + Minimize 2 i=1 * * s.t. yi · ( 〈 w, xi 〉 + b ) ≥ 1 ̶ ξ i for all i ∈ {1,...,n } 2 ξi ≥0 for all i ∈ {1,...,kn
- 34. Support Vector Machines ̶ Nonlinear Classifiers ̶
- 35. Support Vector Machines ̶ Nonlinear SeparationQuestion: Is it possible to create nonlinear classifiers?
- 36. Support Vector Machines ̶ Nonlinear SeparationIdea: Map data points into a higher dimensional feature space where a linear separation is possible. Ф Rn Rm
- 37. Nonlinear Transformation Ф original feature space high dimensional feature space Rn Rm
- 38. Kernel FunctionsAssume: For a given set X of training examples we know a function Ф, such that a linear separation in the high-dimensional space is possible.Decision: When we have solved the corresponding optimization problem, we only need to evaluate a scalar product to decide about the class label of a new data object. n f(xnew) = sign ( Σ α*i yi 〈 Ф (xi), Ф(xneu) 〉 + b* ) ∈ {-1, +1} i=1
- 39. Kernel functionsIntroduce a kernel function K(xi, xj) = 〈 Ф (xi), Ф(xj) 〉The kernel function defines a similarity measure between the objects xi and xj.It is not necessary to know the function Ф or the dimension of H !!!
- 40. Kernel TrickExample: Transformation into a higher dimensional feature space ___ Ф (x1,x2) = ( √ 2 2 Ф:R →R, 2 3 x1 , 2 x1 x2, x2 )Input: An element of the training sample x, ^ a new object x ___ ___ ^ ^2 ), ( x1 ,√ 2 ^ 1 x2, ^ 2 ) 〉 x ^ x 2 2 2 〈 Ф ( x ), Ф( x ) 〉 = 〈 ( x1 , √ 2 x1 x2, x2 x1 ^ 1 + 2 x1 ^ 1 x2 ^ 2 + x2 x2 ^ 2 2 2 = x x x 2 2 = ( x1 ^1 + x2 ^2) x x = K ( x ,^) 2 = 〈 x,^ 〉 x x The scalar product in the higher dimensional space (here: R 3 ) can be evaluated in the low dimensional original space (here: R 2 ).
- 41. Kernel TrickIt is not necessary to apply the nonlinear function Ф to transformthe set of training examples into a higher dimensional feature space.Use a kernel function K(xi, xj) = 〈 Ф (xi), Ф(xj) 〉instead of the scalar product in the original optimization problem and the decision problem.
- 42. Kernel FunctionsLinear kernel K(xi, xj) = 〈 xi, xj 〉 2 ̶ ‖ xi ̶ xj ‖ 2Radial basis function kernel K(xi, xj) = exp ___________ ; σ0 = mean ‖ xi ̶ xj ‖ 2 2 2 σ0Polynomial kernel K(xi, xj) = (s 〈 xi, xj 〉 + c) dSigmoid kernel K(xi, xj) = tanh (s 〈 xi, xj 〉 + c)Convex combinations of kernels K(xi, xj) = c1K1(xi, xj) + c2K2(xi, xj) , K (xi, xj)Normalization kernel K(xi, xj) = ___________________ , , √ K (xi, xi) K (xj, xj)
- 43. Summary• Support vector machines can be used for binary classification.• We can handle misclassified data if we introduce slack variables.• If the sets to discriminate are not linearly separable we can use kernel functions.• Applications → binary decisions − Spam filter (spam / no spam) − Face recognition ( access / no access) − Credit rating ( good customer / bad costumer)
- 44. Literature• N. Christianini, J.Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge, 2004.• T. Hastie, R. Tibshirani, J. Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, 2011.
- 45. Thank you very much!

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