1. CpSc 810: Machine Learning Instance Based Learning

2. 2 Copy Right Notice Most slides in this presentation are adopted from slides of text book and various sources. The Copyright belong to the original authors. Thanks!

3. 3 Instance Based Learning (IBL) IBL methods learn by simply storing the presented training data. When a new query instance is encountered, a set of similar related instances is retrieved from memory and used to classify the new query instance. IBL approaches can construct a different approximation to the target function for each distinct query. They can construct local rather than global approximations. IBL methods can use complex symbolic representations for instances. This is called Case-Based Reasoning (CBR).

4. 4 Advantages and Disadvantages of IBL Methods Advantage: IBL Methods are particularly well suited to problems in which the target function is very complex, but can still be described by a collection of less complex local approximations. Disadvantage I: The cost of classifying new instances can be high (since most of the computation takes place at this stage). Disadvantage II: Many IBL approaches typically consider all attributes of the instances ==> they are very sensitive to the curse of dimensionality!

5. 5 Instance Based Learning Nearest Neighbor: Given query instance x q, first locate nearest training example x n, then estimate f(x q )<-f(x n ) K-Nearest Neighbor: Given query instance x q, take vote among its k nearest neighbors, if discrete- valued target function Take mean of f values of k nearest neighbors, if real valued

6. 6 k-Nearest Neighbor Learning in Euclidean Space Assumption: All instances, x, correspond to points in the n-dimensional space R n. x =. Measure Used: Euclidean Distance: d(x i,x j )=  r=1 n (a r (x i )-a r (x j )) 2 Training Algorithm: For each training example, add the example to the list training_examples. Classification Algorithm: Given a query instance x q to be classified: Let x 1 …x k be the k instances from training_examples that are nearest to x q. Return f ^ (x q ) <- argmax v  V  r=1 n  (v,f(x i )) where  (a,b)=1 if a=b and  (a,b)=0 otherwise.

7. 7 Voronoi Diagram + + - - - : query, x q 1-NN: + 5-NN: - Decision Surface for 1-NN

8. 8 Behavior in the Limit Consider p(x) defines probability that instance x will be labeled 1 (positive) versus 0 (negative) Nearest neighbor: As number of training examples -> ∞, approaches Gibbs Algorithm Gibbs: with probability p(x) predict 1, else 0 K nearest neighbor: As number of training examples -> ∞ and k get large, approaches Bayes optimal Bayes optimal: if p(x)>0.5 then predict 1, else 0 Note Gibbs has at most twice the expected error of Bayes optimal.

9. 9 Distance-Weighted Nearest Neighbors k-NN can be refined by weighting the contribution of the k neighbors according to their distance to the query point x q, giving greater weight to closer neighbors. To do so, replace the last line of the algorithm with f ^ (x q ) <- argmax v  V  r=1 n w i  (v,f(x i )) where w i =1/d(x q,x i ) 2

10. 10 Remarks on kNN Advantages: the NN algorithm can estimate complex target concepts locally and differently for each new instance to be classified; the NN algorithm provides good generalisation accuracy on many domains; the NN algorithm learns very quickly; the NN algorithm is robust to noisy training data; the NN algorithm is intuitive and easy to understand which facilitates implementation and modification.Disadvantages: the NN algorithm has large storage requirements because it has to store all the data; the NN algorithm is slow during instance classification because all the training instances have to be visited; the accuracy of the NN algorithm degrades with increase of noise in the training data; the accuracy of the NN algorithm degrades with increase of irrelevant attributes. Efficient memory indexing of the training instances was proposed to speed up instance classification. The most popular indexing technique is based on multidimensional trees

11. 11 Curse of Dimensionality Inductive Bias of k-nearest neighbor Assumption that the classification of an instance x q will be most similar to the classification of other instance that are nearby in Euclidean distance. Curse of dimensionality: nearest neighbor is easily mislead while high-dimensional X. The distance is calculated based on all attributes of the instance. Image instances described by 20 attributes, but only two are relevant to target function. Solution: weigh the attributes differently (use cross-validation to determine the weights) eliminate the least relevant attributes (again, use cross-validation to determine which attributes to eliminate)

12. 12 Locally Weighted Regression kNN forms local approximation to f for each query point x q, Why not form an explicit approximation f ^ (x) for region surrounding x q Locally weighted regression generalizes nearest-neighbour approaches by constructing an explicit approximation to f over a local region surrounding x q. In such approaches, the contribution of each training example is weighted by its distance to the query point.

13. 13 An Example: Locally Weighted Linear Regression f is approximated by: f ^ (x)=w 0 +w 1 a 1 (x)+…+w n a n (x) Gradient descent can be used to find the coefficients w 0, w 1,…w n that minimize some error function. The error function, however, should be different from the one used in the Neural Net since we want a local solution. Different possibilities: Minimize the squared error over just the k nearest neighbors. Minimize the squared error over the entire training set but weigh the contribution of each example by some decreasing function K of its distance from x q. Combine 1 and 2

14. 14 Radial Basis Function (RBF) Approximating Function: f ^ (x)=w 0 +  u=1 k w u K u (d(x u,x)) K u (d(x u,x)) is a kernel function that decreases as the distance d(x u,x) increases (e.g., the Gaussian function); and k is a user-defined constant that specifies the number of kernel functions to be included. Although f ^ (x) is a global approximation to f(x) the contribution of each kernel function is localized. RBF can be implemented in a neural network. It is a very efficient two step algorithm: Find the parameters of the kernel functions (e.g., use the EM algorithm) Learn the linear weights of the kernel functions.

15. 15 Case-Based Reasoning (CBR) CBR is similar to k-NN methods in that: They are lazy learning methods in that they defer generalization until a query comes around. They classify new query instances by analyzing similar instances while ignoring instances that are very different from the query. However, CBR is different from k-NN methods in that: They do not represent instances as real-valued points, but instead, they use a rich symbolic representation. CBR can thus be applied to complex conceptual problems such as the design of mechanical devices or legal reasoning Application of CBR: Design: landscape, building, mechanical, conceptual design of aircraft sub-systems Planning:repair schedules Diagnosis: medical Adversarial reasoning:legal

16. 16 Case-Based Reasoning (CBR) Methodology Instances represented by rich symbolic descriptions (e.g., function graphs) Search for similar cases, multiple retrieved cases may be combined Tight coupling between case retrieval, knowledge-based reasoning, and problem solving Challenges Find a good similarity metric Indexing based on syntactic similarity measure, and when failure, backtracking, and adapting to additional cases

17. 17 CBR process New Case matching Matched Cases Retrieve Adapt? No Yes Closest Case Suggest solution Retain Learn Revise Reuse Case Base Knowledge and Adaptation rules

18. 18 CBR example: Property pricing Test instance

19. 19 How rules are generated There is no unique way of doing it. Here is one possibility: Examine cases and look for ones that are almost identical case 1 and case 2 R1: If recep-rooms changes from 2 to 1 then reduce price by £5,000 case 3 and case 4 R2: If Type changes from semi to terraced then reduce price by £7,000

20. 20 Matching Comparing test instance matches(5,1) = 3 matches(5,2) = 3 matches(5,3) = 2 matches(5,4) = 1 Estimate price of case 5 is £25,000

21. 21 Adapting Reverse rule 2 if type changes from terraced to semi then increase price by £7,000 Apply reversed rule 2 new estimate of price of property 5 is £32,000

22. 22 Learning So far we have a new case and an estimated price nothing is added yet to the case base If later we find house sold for £35,000 then the case would be added could add a new rule if location changes from 8 to 7 increase price by £3,000

23. 23 December 18, 2015 About CBR Problems with CBR How should cases be represented? How should cases be indexed for fast retrieval? How can good adaptation heuristics be developed? When should old cases be removed? Advantages A local approximation is found for each test case Knowledge is in a form understandable to human beings Fast to train

24. 24 Lazy vs. Eager Learning Eager Learning Learning = acquiring explicit description of the target concepts on the whole training set; Classification = an instance gets a classification using the explicit description of the target concepts. Instance-Based Learning (Lazy Learning) Learning = storing all training instances Classification = an instance gets a classification equal to the classification of the nearest instances to the instance. Accuracy Lazy method effectively uses a richer hypothesis space since it uses many local linear functions to form its implicit global approximation to the target function Eager: must commit to a single hypothesis that covers the entire instance space