Transcript Clustering - University of Kentucky

Classification
CS 685: Special Topics in Data Mining
Spring 2009
Jinze Liu
The UNIVERSITY
of Mining,
KENTUCKY
CS685 : Special
Topics in Data
UKY
Classification and Prediction
•
•
•
•
What is classification? What is regression?
Issues regarding classification and prediction
Classification by decision tree induction
Scalable decision tree induction
CS685 : Special Topics in Data Mining, UKY
Classification vs. Prediction
• Classification:
– 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
• Regression:
– models continuous-valued functions, i.e., predicts
unknown or missing values
• Typical Applications
–
–
–
–
credit approval
target marketing
medical diagnosis
treatment effectiveness analysis
CS685 : Special Topics in Data Mining, UKY
Why Classification? A motivating
application
• Credit approval
– A bank wants to classify its customers based on whether
they are expected to pay back their approved loans
– The history of past customers is used to train the classifier
– The classifier provides rules, which identify potentially
reliable future customers
– Classification rule:
• If age = “31...40” and income = high then credit_rating = excellent
– Future customers
• Paul: age = 35, income = high  excellent credit rating
• John: age = 20, income = medium  fair credit rating
CS685 : Special Topics in Data Mining, UKY
Classification—A Two-Step Process
• Model construction: describing a set of predetermined
classes
– 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 training set
– The model is represented as classification rules, decision trees, or
mathematical formulae
• Model usage: for classifying future or unknown objects
– Estimate accuracy of the model
• 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
• Test set is independent of training set
• If the accuracy is acceptable, use the model to classify data tuples whose
class labels are not known
CS685 : Special Topics in Data Mining, UKY
Classification Process (1):
Model Construction
Classification
Algorithms
Training
Data
NAME RANK
M ike
M ary
B ill
Jim
D ave
Anne
A ssistan t P ro f
A ssistan t P ro f
P ro fesso r
A sso ciate P ro f
A ssistan t P ro f
A sso ciate P ro f
YEARS TENURED
3
7
2
7
6
3
no
yes
yes
yes
no
no
Classifier
(Model)
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’
CS685 : Special Topics in Data Mining, UKY
Classification Process (2): Use
the Model in Prediction
Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
NAME
Tom
M erlisa
G eorge
Joseph
RANK
Y E A R S TE N U R E D
A ssistant P rof
2
no
A ssociate P rof
7
no
P rofessor
5
yes
A ssistant P rof
7
yes
Tenured?
CS685 : Special Topics in Data Mining, UKY
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
CS685 : Special Topics in Data Mining, UKY
Major Classification Models
•
•
•
•
•
•
Classification by decision tree induction
Bayesian Classification
Neural Networks
Support Vector Machines (SVM)
Classification Based on Associations
Other Classification Methods
–
–
–
–
KNN
Boosting
Bagging
…
CS685 : Special Topics in Data Mining, UKY
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
• Goodness of rules
– decision tree size
– compactness of classification rules
CS685 : Special Topics in Data Mining, UKY
Decision Tree
Training
Dataset
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income student credit_rating
high
no
fair
high
no
excellent
high
no
fair
medium
no
fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no
fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no
excellent
high
yes fair
medium
no
excellent
buys_computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no
CS685 : Special Topics in Data Mining, UKY
Output: A Decision Tree for
“buys_computer”
age?
<=30
student?
overcast
30..40
yes
>40
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income student credit_rating
high
no
fair
high
no
excellent
high
no
fair
medium
no
fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no
fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no
excellent
high
yes fair
medium
no
excellent
buys_compu
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no
credit rating?
no
yes
excellent
fair
no
yes
no
yes
CS685 : Special Topics in Data Mining, UKY
Algorithm for Decision Tree
Induction
• Basic algorithm (a greedy algorithm)
– Tree is constructed in a top-down recursive divide-and-conquer
manner
– At 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)
• 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
CS685 : Special Topics in Data Mining, UKY
Attribute Selection Measure:
Information Gain (ID3/C4.5)



Select the attribute with the highest information gain
S contains si tuples of class Ci for i = {1, …, m}
information measures info required to classify any
arbitrary tuple
m
I( s1,s2,...,sm )  
i 1

si
si
log 2
s
s
entropy of attribute A with values {a1,a2,…,av}
s1 j  ... smj
I ( s1 j ,...,smj )
s
j 1
v
E(A) 

information gained by branching on attribute A
Gain(A) I(s1, s 2 ,...,sm)  E(A)
CS685 : Special Topics in Data Mining, UKY
Attribute Selection by
Information Gain Computation




Class P: buys_computer = “yes”
Class N: buys_computer = “no”
I(p, n) = I(9, 5) =0.940
Compute the entropy for age:
age
<=30
30…40
>40
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income
high
high
high
medium
low
low
low
medium
low
medium
medium
medium
high
medium
5
4
I ( 2,3) 
I ( 4,0)
14
14
5

I (3,2)  0.694
14
E ( age) 
pi
2
4
3
ni I(pi, ni)
5
I ( 2,3) means “age <=30” has 5
3 0.971
14
out of 14 samples, with 2
0 0
yes’es and 3 no’s. Hence
2 0.971
student credit_rating buys_computer
no
fair
no
Gain(age)  I ( p, n)  E (age)  0.246
no
no
no
yes
yes
yes
no
yes
yes
yes
no
yes
no
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no
Similarly,
Gain(income)  0.029
Gain( student )  0.151
Gain(credit _ rating )  0.048
CS685 : Special Topics in Data Mining, UKY
Splitting the samples using age
age?
>40
<=30
30...40
income student credit_rating
high
no fair
high
no excellent
medium
no fair
low
yes fair
medium yes excellent
buys_computer
no
no
no
yes
yes
income student credit_rating
high
no fair
low
yes excellent
medium
no excellent
high
yes fair
income student credit_rating
medium
no fair
low
yes fair
low
yes excellent
medium yes fair
medium
no excellent
buys_computer
yes
yes
yes
yes
buys_computer
yes
yes
no
yes
no
labeled yes
CS685 : Special Topics in Data Mining, UKY
Natural Bias in The Information
Gain Measure
• Favor attributes with many values
• An extreme example
– Attribute “income” might have the highest
information gain
– A very broad decision tree of depth one
– Inapplicable to any future data
CS685 : Special Topics in Data Mining, UKY
Alternative Measures
• Gain ratio: penalize attributes like income by
incorporating split information
–
c
| Si |
|S |
log2 i
|S|
i 1 | S |
SplitInform ation( S , A)  
• Split information is sensitive to how broadly and
uniformly the attribute splits the data
–
GainRatio( S , A) 
Gain( S , A)
SplitInform ation( S , A)
• Gain ratio can be undefined or very large
– Only test attributes with above average Gain
CS685 : Special Topics in Data Mining, UKY
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
CS685 : Special Topics in Data Mining, UKY
Gini Index (IBM IntelligentMiner)
• If a data set T contains examples from n classes, gini index, gini(T)
n
is defined as
2
gini (T )  1  p j
j 1
where pj is the relative frequency of class j in T.
• If a data set T is split into two subsets T1 and T2 with sizes N1 and
N2 respectively, the gini index of the split data contains examples
from n classes, the gini index gini(T) is defined as
N1 gini( )  N 2 gini( )
(
T
)

gini split
T1
T2
N
N
• The attribute provides the smallest ginisplit(T) is chosen to split the
node (need to enumerate all possible splitting points for each
attribute).
CS685 : Special Topics in Data Mining, UKY
Comparing Attribute Selection
Measures
• The three measures, in general, return good results but
– Information gain:
• biased towards multivalued attributes
– Gain ratio:
• tends to prefer unbalanced splits in which one partition
is much smaller than the others
– Gini index:
• biased to multivalued attributes
• has difficulty when # of classes is large
• tends to favor tests that result in equal-sized partitions
and purity in both partitions
CS685 : Special Topics in Data Mining, UKY
Extracting Classification Rules from Trees
• Represent the knowledge in the form of IF-THEN rules
• One rule is created for each path from the root to a 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
• Example
IF age = “<=30” AND student = “no” THEN buys_computer = “no”
IF age = “<=30” AND student = “yes” THEN buys_computer = “yes”
IF age = “31…40”
THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “excellent” THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “fair” THEN buys_computer = “no”
CS685 : Special Topics in Data Mining, UKY
Avoid Overfitting in
Classification
• Overfitting: 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 avoid overfitting
– Prepruning: Halt tree construction early—do not split a node if
this would result in the goodness measure falling below a
threshold
• Difficult to choose an appropriate threshold
– Postpruning: Remove branches from a “fully grown” tree—get a
sequence of progressively pruned trees
• Use a set of data different from the training data to decide
which is the “best pruned tree”
CS685 : Special Topics in Data Mining, UKY
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
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Minimum Description Length
• The ideal MDL select the model with the
shortest effective description that minimizes
the sum of
– The length, in bits, of an effective description of
the model; and
– The length, in bits, of an effective description of
the data when encoded with help of the model
H 0  minK ( D | H )  K ( H )
H 
CS685 : Special Topics in Data Mining, UKY
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
CS685 : Special Topics in Data Mining, UKY
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
CS685 : Special Topics in Data Mining, UKY
Scalable Decision Tree Induction Methods
in Data Mining Studies
• SLIQ (EDBT’96 — Mehta et al.)
– builds an index for each attribute and only class list and the current
attribute list reside in memory
• SPRINT (VLDB’96 — J. Shafer et al.)
– constructs an attribute list data structure
• PUBLIC (VLDB’98 — Rastogi & Shim)
– integrates tree splitting and tree pruning: stop growing the tree earlier
• RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti)
– separates the scalability aspects from the criteria that determine the
quality of the tree
– builds an AVC-list (attribute, value, class label)
CS685 : Special Topics in Data Mining, UKY