1. Business Systems Intelligence: 5. Classification 1
Dr. Brian Mac Namee (
Business Systems Intelligence: 5. Classification 1

2. And information on SAS is available at: www.sas.com
Acknowledgments
These notes are based (heavily) on those provided by the authors to accompany “Data Mining: Concepts & Techniques” by Jiawei Han and Micheline Kamber
Some slides are also based on trainer’s kits provided by
More information about the book is available at: www-sal.cs.uiuc.edu/~hanj/bk2/
And information on SAS is available at:

3. Classification & Prediction
Today we will look at:
What are classification & prediction?
Issues regarding classification and prediction
Classification techniques:
Case based reasoning (k-nearest neighbour algorithm)
Decision tree induction
Bayesian classification
Neural networks
Support vector machines (SVM)
Classification based on association rule mining concepts
Other classification methods
Prediction
Classification accuracy

4. Classification & Prediction
Predicts categorical class labels
Classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data
Prediction:
Models continuous-valued functions, i.e., predicts unknown or missing values
Typical Applications
Credit approval
Target marketing
Medical diagnosis
Treatment effectiveness analysis

5. Classification: A Two-Step Process
1) Model construction:
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute
The set of tuples used for model construction is the training set
A model created for classification

6. Classification: A Two-Step Process (cont…)
2) Model usage:
Estimate accuracy of the model
All members of an independent test-set is tested using the model built
The known label of test sample is compared with the classified result from the model
Accuracy rate is the percentage of test set samples that are correctly classified by the model
If the accuracy is acceptable, the model is used to classify data tuples whose class labels are not known

7. Classification: Model Construction
Classification Algorithm
Training Set
Classification Model
NAME
RANK
YEARS
TENURED
Mike
Assistant Prof 3 no
Mary 7
yes
Bill
Professor 2
Jim
Associate Prof
Dave 6
Anne
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’

8. Classification: Using The Model In Prediction
Classifier
Testing Set
Unseen Data
(Jeff, Professor, 4)
NAME
RANK
YEARS
TENURED
Tom
Assistant Prof 2 no
Merlisa
Associate Prof 7
George
Professor 5
yes
Joseph
Tenured?
Yes

9. Supervised Vs. Unsupervised Learning
Supervised learning (classification)
Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
New data is classified based on the training set
Unsupervised learning (clustering)
The class labels of training data is unknown
Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data

10. Issues Regarding Classification & Prediction: Data Preparation
Data cleaning
Preprocess data in order to reduce noise and handle missing values
Relevance analysis (feature selection)
Remove the irrelevant or redundant attributes
Data transformation
Generalize and/or normalize data

11. Predictive accuracy Speed and scalability Robustness Scalability
Issues Regarding Classification & Prediction: Evaluating Classification Methods
Predictive accuracy
Speed and scalability
Time to construct the model
Time to use the model
Robustness
Handling noise and missing values
Scalability
Efficiency in disk-resident databases
Interpretability
Understanding and insight provided by the model

12. Classification Techniques: Case Based Reasoning (The k-Nearest Neighbor Algorithm)
Case based reasoning is a classification technique which uses prior examples (cases) to determine the classification of unknown cases
The k-nearest neighbour (k-NN) algorithm is the simplest form of case based reasoning

13. The k-Nearest Neighbor Algorithm)
All instances correspond to points in n-D space
The nearest neighbours are defined in terms of Euclidean distance (or other appropriate measure)
The target value can be discrete or real-valued
For discrete targets, k-NN returns the most common value among the k training examples nearest to the query
For real-valued targets, k-NN returns a combination (e.g. average) of the nearest neighbours’ target values

14. Nearest Neighbour Example
Features
Class
Wave Size (ft)
Wave Period (secs) 6 15 1 5 11 7 10 2 3 4 12
Good Surf?
Yes No
Query 10 ?

15. Nearest Neighbour Example
When a new case is to be classified:
Calculate the distance from the new case to all training cases
Put the new case in the same class as its nearest neighbour f1 ?
Wave Size ? ?
Wave Period f2

16. k-Nearest Neighbour Example
What about when it’s too close to call?
Use the k-nearest neighbour technique
Determine the k nearest neighbours to the query case
Put the new case into the same class as the majority of its nearest neighbours f1 2
neighbours 1
neighbour
vs.
Wave Size ?
Wave Period f2

17. Nearest Neighbour Distance Measures
Any kind of measurement can be used to calculate the distance between cases
The measurement most suitable will depend on the type of features in the problem
Euclidean distance is the most used technique
where n is the number of features, ti is the ith feature of the training case and qi is the ith feature of the query case

18. Summary Of Nearest Neighbour Classification
Strengths
No training involved – lazy learning
New data can be added on the fly
Some explanation capabilities
Robust to noisy data by averaging k-nearest neighbors
Weaknesses
Not the most powerful classification
Slow classification
Curse of dimensionality
One of the easiest machine learning classification techniques to understand

19. Case-Based Reasoning
Uses lazy evaluation and analysis of similar instances
However, instances are not necessarily “points in a Euclidean space”
Methodology
Instances represented by rich symbolic descriptions
Multiple retrieved cases may be combined
Tight coupling between case retrieval, knowledge-based reasoning, and problem solving
Lots of active research issues

20. Classification Techniques: Decision Tree Induction
Decision trees are the most widely used classification technique in data mining today
Formulate problems into a tree composed of decision nodes (or branch nodes) and classification nodes (or leaf nodes)
Problem is solved by navigating down the tree until we reach an appropriate leaf node
The tricky bit is building the most efficient and powerful tree
J. Ross Quinlan is a famed researcher in data mining and decision theory. He has done pioneering work in the area of decision trees, including inventing the ID3 and C4.5 algorithms.

21. Training Dataset Age Income Student CreditRating BuysComputer <=30
high no
fair
excellent
yes
>40
medium
low

22. Resultant Decision Tree
Age?
<=30
>40
Student?
Yes
Credit Rating? no
yes
excellent
fair No
Yes No
Yes

23. Algorithm For Decision Tree Induction
Basic algorithm (a greedy algorithm)
Tree is constructed in a top-down recursive divide-and-conquer manner
At the start, all the training examples are at the root
Attributes are categorical (if continuous-valued, they are discretized in advance)
Examples are partitioned recursively based on selected attributes
Test attributes are selected on the basis of a heuristic or statistical measure (e.g. information gain)

24. Algorithm For Decision Tree Induction
Conditions for stopping partitioning
All samples for a given node belong to the same class
There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf
There are no samples left

25. Attribute Selection Measure: Information Gain (ID3/C4.5)
The attribute selection mechanism used in ID3 and based on work on information theory by Claude Shannon
If our data is split into classes according to fractions {p1,p2…, pm} then the entropy is measured as the info required to classify any arbitrary tuple as follows:

26. Attribute Selection Measure: Information Gain (ID3/C4.5) (cont…)
The information measure is essentially the same as entropy
At the root node the information is as follows:

27. Attribute Selection Measure: Information Gain (ID3/C4.5) (cont…)
To measure the information at a particular attribute we measure info for the various splits of that attribute

28. Attribute Selection Measure: Information Gain (ID3/C4.5) (cont…)
At the age attribute the information is as follows:

29. Attribute Selection Measure: Information Gain (ID3/C4.5) (cont…)
In order to determine which attributes we should use at each node we measure the information gained in moving from one node to another and choose the one that gives us the most information

30. Attribute Selection By Information Gain Example
Class P: BuysComputer = “yes”
Class N: BuysComputer = “no”
I(p, n) = I(9, 5) =0.940
Compute the entropy for age:
Age
Income
Student
CreditRating
BuysComputer
<=30
high no
fair
excellent
yes
>40
medium
low
Age pi ni
I(pi, ni)
>=30 2 3
0.971
30 – 40 4
>40

31. Attribute Selection By Information Gain Computation
means “age <=30” has 5 out of 14 samples, with 2 yes and 3 no. Hence:
Similarly:

32. Other Attribute Selection Measures
Gini index (CART, IBM IntelligentMiner)
All attributes are assumed continuous-valued
Assume there exist several possible split values for each attribute
May need other tools, such as clustering, to get the possible split values
Can be modified for categorical attributes

33. Extracting Classification Rules From Trees
Represent knowledge in the form of IF-THEN rules
One rule is created for each path from root to leaf
Each attribute-value pair along a path forms a conjunction
The leaf node holds the class prediction
Rules are easier for humans to understand
IF Age = “<=30” AND Student = “no” THEN BuysComputer = “no”
IF Age = “<=30” AND Student = “yes” THEN BuysComputer = “yes”
IF Age = “31…40” THEN BuysComputer = “yes”
IF Age = “>40” AND CreditRating = “excellent” THEN BuysComputer = “yes”
IF Age = “<=30” AND CreditRating = “fair” THEN BuysComputer = “no”

34. Overfitting
Training Set
Test Set

35. Overfitting (cont…)
Training Set
Test Set

36. Avoiding Overfitting In Classification
An induced tree may overfit the training data
Too many branches, some may reflect anomalies due to noise or outliers
Poor accuracy for unseen samples
Two approaches to avoiding overfitting
Prepruning: Halt tree construction early
Do not split a node if this would result in a measure of the usefullness of the tree falling below a threshold
Difficult to choose an appropriate threshold
Postpruning: Remove branches from a “fully grown” tree to give a sequence of progressively pruned trees
Use a set of data different from the training data to decide which is the “best pruned tree”

37. Approaches To Determine The Final Tree Size
Separate training (2/3) and testing (1/3) sets
Use cross validation, e.g., 10-fold cross validation
Use all the data for training
But apply a statistical test (e.g., chi-square) to estimate whether expanding or pruning a node may improve the entire distribution
Use minimum description length (MDL) principle
Halting growth of the tree when the encoding is minimized

38. Enhancements To Basic Decision Tree Induction
Allow for continuous-valued attributes
Dynamically define new discrete-valued attributes that partition the continuous attribute value into a discrete set of intervals
Handle missing attribute values
Assign the most common value of the attribute
Assign probability to each of the possible values
Attribute construction
Create new attributes based on existing ones that are sparsely represented
This reduces fragmentation, repetition, and replication

39. Classification In Large Databases
Classification - a classical problem extensively studied by statisticians and machine learning researchers
Scalability: Classifying data sets with millions of examples and hundreds of attributes with reasonable speed
Why decision tree induction in data mining?
Relatively faster learning speed (than other classification methods)
Convertible to simple and easy to understand classification rules
Can use SQL queries for accessing databases
Comparable classification accuracy with other methods

40. Data Cube-Based Decision-Tree Induction
Integration of generalization with decision-tree induction
Classification at primitive concept levels
E.g., precise temperature, humidity, outlook, etc.
Low-level concepts, scattered classes, bushy classification-trees
Semantic interpretation problems
Cube-based multi-level classification
Relevance analysis at multi-levels
Information-gain analysis with dimension + level

41. Decision Tree In SAS

42. Bayesian Classification: Why?
Probabilistic learning:
Calculate explicit probabilities for a hypothesis
Among the most practical approaches to certain types of learning problems
Incremental:
Each training example can incrementally increase/ decrease the probability that a hypothesis is correct
Prior knowledge can be combined with observed data
Probabilistic prediction:
Predict multiple hypotheses, weighted by their probabilities
Standard:
Bayesian methods can provide a standard of optimal decision making against which other methods can be measured

43. Bayesian Theorem: Basics
Let X be a data sample whose class label is unknown
Let H be a hypothesis that X belongs to class C
For classification problems, determine P(H|X): the probability that the hypothesis holds given the observed data sample X
P(H): prior probability of hypothesis H (i.e. the initial probability before we observe any data, reflects the background knowledge)
P(X): probability that sample data is observed
P(X|H): probability of observing the sample X, given that the hypothesis holds

44. posterior = (likelihood * prior) / evidence
Bayesian Theorem
Given training data X, posteriori probability of a hypothesis H, P(H|X) follows the Bayes theorem
Informally, this can be written as
MAP (maximum posteriori) hypothesis
Practical difficulty: require initial knowledge of many probabilities, significant computational cost
posterior = (likelihood * prior) / evidence

45. Naïve Bayes Classifier
A simplified assumption: attributes are conditionally independent:
The product of occurrence of say 2 elements x1 and x2, given the current class is C, is the product of the probabilities of each element taken separately, given the same class P([y1,y2],C) = P(y1,C) * P(y2,C)
No dependence relation between attributes
Greatly reduces the computation cost, only count the class distribution.
Once the probability P(X|Ci) is known, assign X to the class with maximum P(X|Ci)*P(Ci)

46. Training dataset Class: C1:buys_computer= ‘yes’ C2:buys_computer= ‘no’
Data sample
X =(age<=30,
Income=medium,
Student=yes
Credit_rating=
Fair)

47. Naïve Bayesian Classifier: Example
Compute P(X/Ci) for each class
P(age=“<30” | buys_computer=“yes”) = 2/9=0.222
P(age=“<30” | buys_computer=“no”) = 3/5 =0.6
P(income=“medium” | buys_computer=“yes”)= 4/9 =0.444
P(income=“medium” | buys_computer=“no”) = 2/5 = 0.4
P(student=“yes” | buys_computer=“yes)= 6/9 =0.667
P(student=“yes” | buys_computer=“no”)= 1/5=0.2
P(credit_rating=“fair” | buys_computer=“yes”)=6/9=0.667
P(credit_rating=“fair” | buys_computer=“no”)=2/5=0.4
X=(age<=30 ,income =medium, student=yes,credit_rating=fair)
P(X|Ci) : P(X|buys_computer=“yes”)= x x x =0.044
P(X|buys_computer=“no”)= 0.6 x 0.4 x 0.2 x 0.4 =0.019
P(X|Ci)*P(Ci ) : P(X|buys_computer=“yes”) * P(buys_computer=“yes”)=0.028
P(X|buys_computer=“no”) * P(buys_computer=“no”)=0.007
X belongs to class “buys_computer=yes”

48. Naïve Bayesian Classifier: Comments
Advantages :
Easy to implement
Good results obtained in most of the cases
Disadvantages
Assumption: class conditional independence , therefore loss of accuracy
Practically, dependencies exist among variables
E.g., hospitals: patients: Profile: age, family history etc
Symptoms: fever, cough etc., Disease: lung cancer, diabetes etc
Dependencies among these cannot be modeled by Naïve Bayesian Classifier
How to deal with these dependencies?
Bayesian Belief Networks

49. Bayesian Networks
Bayesian belief network allows a subset of the variables conditionally independent
A graphical model of causal relationships
Represents dependency among the variables
Gives a specification of joint probability distribution
Nodes: random variables
Links: dependency
X,Y are the parents of Z, and Y is the parent of P
No dependency between Z and P
Has no loops or cycles Y Z P X

50. Bayesian Belief Network: An Example
Family
History
Smoker
(FH, S)
(FH, ~S)
(~FH, S)
(~FH, ~S) LC
0.8
0.5
0.7
0.1
LungCancer
Emphysema
~LC
0.2
0.5
0.3
0.9
The conditional probability table for the variable LungCancer:
Shows the conditional probability for each possible combination of its parents
PositiveXRay
Dyspnea
Bayesian Belief Networks

51. Learning Bayesian Networks
Several cases
Given both the network structure and all variables observable: learn only the CPTs
Network structure known, some hidden variables: method of gradient descent, analogous to neural network learning
Network structure unknown, all variables observable: search through the model space to reconstruct graph topology
Unknown structure, all hidden variables: no good algorithms known for this purpose
D. Heckerman, Bayesian networks for data mining

52. Lazy Vs. Eager Learning Lazy learning: Eager learning:
Case based reasoning
Eager learning:
Decision-tree and Bayesian classification
Key differences:
Lazy method may consider query instance when deciding how to generalize beyond the training data D
Eager method cannot since they have already chosen global approximation when seeing the query

53. Lazy Vs. Eager Learning Efficiency: Accuracy:
Lazy, less time training but more time predicting
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 learners must commit to a single hypothesis that covers the entire instance space
Easier for lazy learners to cope with concept drift

54. Summary Classification is an extensively studied problem
Classification is probably one of the most widely used data mining techniques with a lot of extensions
Classification techniques can be categorized as either lazy or eager
Scalability is still an important issue for database applications: thus combining classification with database techniques should be a promising topic
Research directions: classification of non-relational data, e.g., text, spatial, multimedia, etc. classification of skewed data sets

55. Questions? ?