1. Data Mining Classification: Alternative Techniques
Lecture Notes for Chapter 5
Introduction to Data Mining by
Tan, Steinbach, Kumar
© Tan,Steinbach, Kumar Introduction to Data Mining /18/

2. Alternative Techniques
Rule-Based Classifier
Classify records by using a collection of “if…then…” rules
Instance Based Classifiers

3. Rule-based Classifier (Example)
R1: (Give Birth = no)  (Can Fly = yes)  Birds
R2: (Give Birth = no)  (Live in Water = yes)  Fishes
R3: (Give Birth = yes)  (Blood Type = warm)  Mammals
R4: (Give Birth = no)  (Can Fly = no)  Reptiles
R5: (Live in Water = sometimes)  Amphibians

4. Application of Rule-Based Classifier
A rule r covers an instance x if the attributes of the instance satisfy the condition of the rule
R1: (Give Birth = no)  (Can Fly = yes)  Birds
R2: (Give Birth = no)  (Live in Water = yes)  Fishes
R3: (Give Birth = yes)  (Blood Type = warm)  Mammals
R4: (Give Birth = no)  (Can Fly = no)  Reptiles
R5: (Live in Water = sometimes)  Amphibians
The rule R1 covers a hawk => Bird
The rule R3 covers the grizzly bear => Mammal

5. How does Rule-based Classifier Work?
R1: (Give Birth = no)  (Can Fly = yes)  Birds
R2: (Give Birth = no)  (Live in Water = yes)  Fishes
R3: (Give Birth = yes)  (Blood Type = warm)  Mammals
R4: (Give Birth = no)  (Can Fly = no)  Reptiles
R5: (Live in Water = sometimes)  Amphibians
A lemur triggers rule R3, so it is classified as a mammal
A turtle triggers both R4 and R5
A dogfish shark triggers none of the rules

6. From Decision Trees To Rules
Rules are mutually exclusive and exhaustive
Rule set contains as much information as the tree

7. Rules Can Be Simplified
Initial Rule: (Refund=No)  (Status=Married)  No
Simplified Rule: (Status=Married)  No

8. Instance-Based Classifiers
Store the training records
Use training records to predict the class label of unseen cases

9. Instance Based Classifiers
Examples:
Rote-learner
Memorizes entire training data and performs classification only if attributes of record match one of the training examples exactly
Nearest neighbor
Uses k “closest” points (nearest neighbors) for performing classification

10. Nearest Neighbor Classifiers
Basic idea:
If it walks like a duck, quacks like a duck, then it’s probably a duck
Training Records
Test Record
Compute Distance
Choose k of the “nearest” records

11. Nearest-Neighbor Classifiers
Requires three things
The set of stored records
Distance Metric to compute distance between records
The value of k, the number of nearest neighbors to retrieve
To classify an unknown record:
Compute distance to other training records
Identify k nearest neighbors
Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote)

12. Definition of Nearest Neighbor
K-nearest neighbors of a record x are data points that have the k smallest distance to x

13. Nearest Neighbor Classification
Compute distance between two points:
Euclidean distance
Determine the class from nearest neighbor list
take the majority vote of class labels among the k-nearest neighbors
Weigh the vote according to distance
weight factor, w = 1/d2

14. Nearest Neighbor Classification…
Choosing the value of k:
If k is too small, sensitive to noise points
If k is too large, neighborhood may include points from other classes

15. Nearest Neighbor Classification…
Scaling issues
Attributes may have to be scaled to prevent distance measures from being dominated by one of the attributes
Example:
height of a person may vary from 1.5m to 1.8m
weight of a person may vary from 90lb to 300lb
income of a person may vary from $10K to $1M
Solution: Normalize the vectors to unit length

16. Nearest neighbor Classification…
k-NN classifiers are lazy learners
It does not build models explicitly
Unlike eager learners such as decision tree induction and rule-based systems
Classifying unknown records are relatively expensive

17. Artificial Neural Networks (ANN)

18. Artificial Neural Networks (ANN)
What is ANN?

19. Artificial Neural Networks (ANN)
Axon
Nucleus
Cell Body
Synapse
Dendrites
Output (Y) w1 w2 w3 b S y x1 x2 x3
Input (X)
Weight
Neuron

20. Artificial Neural Networks (ANN)
Output Y is 1 if at least two of the three inputs are equal to 1.

21. Artificial Neural Networks (ANN)

22. Artificial Neural Networks (ANN)
Model is an assembly of inter-connected nodes and weighted links
Output node sums up each of its input value according to the weights of its links
Compare output node against some threshold t
Perceptron Model or

23. General Structure of ANN
Training ANN means learning the weights of the neurons

24. Algorithm for learning ANN
Initialize the weights (w0, w1, …, wk)
Adjust the weights in such a way that the output of ANN is consistent with class labels of training examples
Objective function:
Find the weights wi’s that minimize the above objective function
e.g., backpropagation algorithm (see lecture notes)

25. Support Vector Machines
Find a linear hyperplane (decision boundary) that will separate the data

26. Support Vector Machines
One Possible Solution

27. Support Vector Machines
Another possible solution

28. Support Vector Machines
Other possible solutions

29. Support Vector Machines
Which one is better? B1 or B2?
How do you define better?

30. Support Vector Machines
Find hyperplane maximizes the margin => B1 is better than B2

31. Support Vector Machines

32. Support Vector Machines
We want to maximize:
Which is equivalent to minimizing:
But subjected to the following constraints:
This is a constrained optimization problem
Numerical approaches to solve it (e.g., quadratic programming)

33. Support Vector Machines
What if the problem is not linearly separable?

34. Support Vector Machines
What if the problem is not linearly separable?
Introduce slack variables
Need to minimize:
Subject to:

35. Nonlinear Support Vector Machines
What if decision boundary is not linear?

36. Nonlinear Support Vector Machines
Transform data into higher dimensional space

37. Ensemble Methods Construct a set of classifiers from the training data
Predict class label of previously unseen records by aggregating predictions made by multiple classifiers

38. General Idea

39. Why does it work? Suppose there are 25 base classifiers
Each classifier has error rate,  = 0.35
Assume classifiers are independent
Probability that the ensemble classifier makes a wrong prediction:

40. Examples of Ensemble Methods
How to generate an ensemble of classifiers?
Bagging
Boosting

41. Bagging Sampling with replacement
Build classifier on each bootstrap sample
Each sample has probability (1 – 1/n)n of being selected

42. Boosting
An iterative procedure to adaptively change distribution of training data by focusing more on previously misclassified records
Initially, all N records are assigned equal weights
Unlike bagging, weights may change at the end of boosting round

43. Boosting
Records that are wrongly classified will have their weights increased
Records that are classified correctly will have their weights decreased
Example 4 is hard to classify
Its weight is increased, therefore it is more likely to be chosen again in subsequent rounds

44. Example: AdaBoost Base classifiers: C1, C2, …, CT Error rate:
Importance of a classifier:

45. Example: AdaBoost Weight update:
If any intermediate rounds produce error rate higher than 50%, the weights are reverted back to 1/n and the resampling procedure is repeated
Classification:

46. Illustrating AdaBoost
Initial weights for each data point
Data points for training

47. Illustrating AdaBoost