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Data Mining Classification: Alternative
Techniques
第 5 章 分類技術
2
Bayes Classifier
1. 貝氏理論2. 利用貝氏理論分類3. 單純貝氏分類法( Naïve Bayes )4. 貝氏信念網路
( Bayesian belief network , BBN)
3
某一事件發生的機率常受其他相關事件是否發生影響,某一事件發生的機率常受其他相關事件是否發生影響,稱為條件機率稱為條件機率 (conditional probability)(conditional probability) 記作記作 P P ((A A | | BB)) 。。某一事件發生的機率常受其他相關事件是否發生影響,某一事件發生的機率常受其他相關事件是否發生影響,稱為條件機率稱為條件機率 (conditional probability)(conditional probability) 記作記作 P P ((A A | | BB)) 。。
條件機率:條件機率:
或或
條件機率:條件機率:
或或
( )( | )
( )P A B
P A BP B
( )
( | )( )
P A BP A B
P B
條件機率 & 乘法律
AP
BAPABP
|
AP
BAPABP
|
乘法律乘法律 (multiplication law)(multiplication law) 用來計算兩事件交集的機率用來計算兩事件交集的機率
及及
乘法律乘法律 (multiplication law)(multiplication law) 用來計算兩事件交集的機率用來計算兩事件交集的機率
及及PP((AA ∩∩BB) = ) = PP((BB))PP((A A | | BB)) PP((AA ∩∩BB) = ) = PP((AA))PP((B B | | AA))
4一般形式的貝氏定理
若事件若事件 BB11,B,B22,…,B,…,Bkk 構成樣本空間構成樣本空間 SS 的一組分割,的一組分割,且且 PP((BBii)≠0,)≠0,ii=1,2,…,=1,2,…,kk ;則對;則對 SS 中的任一事件中的任一事件 PP((AA)≠0)≠0 而言,而言,若事件若事件 BB11,B,B22,…,B,…,Bkk 構成樣本空間構成樣本空間 SS 的一組分割,的一組分割,且且 PP((BBii)≠0,)≠0,ii=1,2,…,=1,2,…,kk ;則對;則對 SS 中的任一事件中的任一事件 PP((AA)≠0)≠0 而言,而言,
1 1 2 2
( ) ( )( )
( ) ( ) ( ) ( ) ( ) ( )i i
ik k
P B P A BP B A
P B P A B P B P A B P B P A B
5
(1) (2) (3) (4) (5)
事件 事前機率 條件機率 聯合機率 事後機率Bi P(Bi) P(A|Bi) P(Bi∩A) P(Bi|A)
B1 0.2 0.05 0.010 0.010/0.032=0.3125
B2 0.3 0.04 0.012 0.012/0.032=0.3750
B3 0.5 0.02 0.010 0.010/0.032=0.3125
1.00 P(A)=0.032 1.000
貝氏定理的列表分析貝氏定理的列表分析
一般形式的貝氏定理例題
6
如果事件 如果事件 AA 發生的機率不受事件發生的機率不受事件 BB 的影響,稱事件的影響,稱事件 AA 和和BB 為獨立事件為獨立事件 (independent events)(independent events) 。。如果事件 如果事件 AA 發生的機率不受事件發生的機率不受事件 BB 的影響,稱事件的影響,稱事件 AA 和和BB 為獨立事件為獨立事件 (independent events)(independent events) 。。
兩事件 兩事件 AA 和 和 BB 是獨立事件,則是獨立事件,則
且且
兩事件 兩事件 AA 和 和 BB 是獨立事件,則是獨立事件,則
且且PP((AA||BB) = ) = PP((AA))
獨立事件 / 條件獨立事件條件獨立事件
條件獨立事件條件獨立事件 (Conditional independence)(Conditional independence) ::在在 MM 事件發生的狀況下,事件發生的狀況下,A,B,CA,B,C 事件發生的機率不受彼此的影響事件發生的機率不受彼此的影響
條件獨立事件條件獨立事件 (Conditional independence)(Conditional independence) ::在在 MM 事件發生的狀況下,事件發生的狀況下,A,B,CA,B,C 事件發生的機率不受彼此的影響事件發生的機率不受彼此的影響
PP((AA ∩∩BB) = ) = PP((AA))PP((BB))
PP((AA ∩ ∩ BB ∩ ∩ C | MC | M)) = P = P((A|MA|M)) P P((B|MB|M)) P P((C|MC|M))
7Bayes theorem
貝氏理論( Bayes theorem ),它是一個從資料當中結合類別知識的方法。
A probabilistic framework for solving classification problems
Conditional Probability:
Bayes theorem:
)()()|(
)|(AP
CPCAPACP
)(),(
)|(
)(),(
)|(
CPCAP
CAP
APCAP
ACP
8Example of Bayes Theorem
Given: – 醫生知道,腦膜炎有 50 %的機率會導致頸部僵硬 – 先驗機率:任何病人患有腦膜炎的機率是 P(M)=1 /
50000
– 先驗機率:有任何病人頸部僵硬的機率是 P(S)=1 / 20
後驗機率:如果病人頸部僵硬,該病人患腦膜炎的機率為何 P(M|S) ?
0002.020/150000/15.0
)()()|(
)|( SP
MPMSPSMP
9Bayesian Classifiers
Consider each attribute and class label as random variables
Given a record with attributes (A1, A2,…,An)
– Goal is to predict class C
– Specifically, we want to find the value of C that maximizes P(C| A1, A2,…,An )
Can we estimate P(C| A1, A2,…,An ) directly from data?
10Bayesian Classifiers
Approach:
– compute the posterior probability P(C | A1, A2, …, An) for all values of C using the Bayes theorem
– Choose value of C that maximizes P(C | A1, A2, …, An)
– Equivalent to choosing value of C that maximizes P(A1, A2, …, An|C) P(C)
How to estimate P(A1, A2, …, An | C )?
)()()|(
)|(21
21
21
n
n
n AAAPCPCAAAP
AAACP
11Naïve Bayes Classifier
Assume independence among attributes Ai when
class is given:
– P(A1, A2, …, An |Cj) = P(A1| Cj) P(A2| Cj)… P(An| Cj)
– Can estimate P(Ai| Cj) for all Ai and Cj.
– New point is classified to Cj if P(Cj) P(Ai| Cj) is maximal.
12How to Estimate Probabilities from Data?
Class: P(C) = Nc/N– e.g., P(No) = 7/10,
P(Yes) = 3/10
For discrete attributes:
P(Ai | Ck) = |Aik|/ Nck
– where |Aik| is number of instances having attribute Ai and belongs to class Ck
– Examples:
P(Status=Married|No) = 4/7P(Refund=Yes|Yes)=0
13Example
P(O)=3/10 P(No|O)=1 P(Div|O)=1/3 P(Low|O)=1/3 P(X)=7/10 P(No|X)=4/7 P(Div|X)=1/7 P(Low|X)=3/7
W={No,Div,Low} Class(W)=O or X ?
Tid RefundMaritalStatus
TaxableIncome
Class
1 Yes Single Mid X
2 No Married Mid X
3 No Single Low X
4 Yes Married Mid X
5 No Divorced Mid O
6 No Married Low X
7 Yes Divorced High X
8 No Single Low O
9 No Married Low X
10 No Single Mid O
14Example
P(O)=3/10 P(No|O)=1 P(Div|O)=1/3 P(Low|O)=1/3
Tid RefundMaritalStatus
TaxableIncome
Class
1 Yes Single Mid X
2 No Married Mid X
3 No Single Low X
4 Yes Married Mid X
5 No Divorced Mid O
6 No Married Low X
7 Yes Divorced High X
8 No Single Low O
9 No Married Low X
10 No Single Mid O
P(X)=7/10 P(No|X)=4/7 P(Div|X)=1/7 P(Low|X)=3/7
W={No,Div,Low} P(O|W)= P(No|O)P(Div|O)P(Low|O)P(O)/P(W)= 1/30/P(W) P(X|W)= P(No|X)P(Div|X)P(Low|X)P(X)/P(W)= 2/245/P(W) ∵P(O|W) > P(X|W) Class(W)=O∴
)(
)()|()|(
21
2121
n
nn AAAP
CPCAAAPAAACP
P(A1, A2, …, An |Cj) = P(A1| Cj) P(A2| Cj)… P(An| Cj)
15Example of Naïve Bayes Classifier
Class
Give Birthmammal
s
non-mammal
s總計
no 1 12 13
yes 6 1 7
總計 7 13 20
Lay Eggsmammal
s
non-mammal
s總計
no 6 1 7
yes 1 12 13
總計 7 13 20
Can Flymammal
s
non-mammal
s總計
no 6 10 16
yes 1 3 4
總計 7 13 20
Live in Water
mammals
non-mammal
s總計
no 5 6 11
sometimes 4 4
yes 2 3 5
總計 7 13 20
Have Legsmammal
s
non-mammal
s總計
no 2 4 6
yes 5 9 14
總計 7 13 20
Name Give Birth Can Fly Live in Water Have Legs Class
human yes no no yes mammalspython no no no no non-mammalssalmon no no yes no non-mammalswhale yes no yes no mammalsfrog no no sometimes yes non-mammalskomodo no no no yes non-mammalsbat yes yes no yes mammalspigeon no yes no yes non-mammalscat yes no no yes mammalsleopard shark yes no yes no non-mammalsturtle no no sometimes yes non-mammalspenguin no no sometimes yes non-mammalsporcupine yes no no yes mammalseel no no yes no non-mammalssalamander no no sometimes yes non-mammalsgila monster no no no yes non-mammalsplatypus no no no yes mammalsowl no yes no yes non-mammalsdolphin yes no yes no mammalseagle no yes no yes non-mammals
Give Birth Can Fly Live in Water Have Legs Class
yes no yes no ?
16Example of Naïve Bayes Classifier
Name Give Birth Can Fly Live in Water Have Legs Class
human yes no no yes mammalspython no no no no non-mammalssalmon no no yes no non-mammalswhale yes no yes no mammalsfrog no no sometimes yes non-mammalskomodo no no no yes non-mammalsbat yes yes no yes mammalspigeon no yes no yes non-mammalscat yes no no yes mammalsleopard shark yes no yes no non-mammalsturtle no no sometimes yes non-mammalspenguin no no sometimes yes non-mammalsporcupine yes no no yes mammalseel no no yes no non-mammalssalamander no no sometimes yes non-mammalsgila monster no no no yes non-mammalsplatypus no no no yes mammalsowl no yes no yes non-mammalsdolphin yes no yes no mammalseagle no yes no yes non-mammals
Give Birth Can Fly Live in Water Have Legs Class
yes no yes no ?
0027.02013
004.0)()|(
021.0207
06.0)()|(
0042.0134
133
1310
131
)|(
06.072
72
76
76
)|(
NPNAP
MPMAP
NAP
MAP
A: attributes
M: mammals
N: non-mammals
P(A|M)P(M) > P(A|N)P(N)
Mammals
18How to Estimate Probabilities from Data?
Normal distribution:
– One for each (Ai,ci) pair
For (Income, Class=No):
– If Class=No μ= 110
σ2= 2975
2
2
2
)(
22
1)|( ij
ijiA
ij
ji ecAP
0072.0)2975(2
1)|120( )2975(2
)110120( 2
eNoIncomeP
20Example
P(O)=4/10 P(Yes|O)=1/4 P(Single|O)=1/4 P(Mid|O)=1/4
Tid RefundMaritalStatus
TaxableIncome
Class
1 Yes Single High X
2 Yes Single High X
3 Yes Single High X
4 Yes Single High X
5 Yes Single High X
6 No Married High X
7 Yes Married Low O
8 No Single Low O
9 No Married Low O
10 No Married Mid O
P(X)=6/10 P(Yes|X)=5/6 P(Single|X)=5/6 P(Mid|X)=0
W={Yes,Single,Mid} P(O|W)= (4/10)(1/4)3/P(W)= (1/160)/P(W) P(X|W)= (6/10)(5/6)2*0/P(W)= 0/P(W) = 0 ∵P(O|W) > P(X|W) Class(W)=O∴
)(
)()|()|(
21
2121
n
nn AAAP
CPCAAAPAAACP
P(A1, A2, …, An |Cj) = P(A1| Cj) P(A2| Cj)… P(An| Cj)
21Naïve Bayes Classifier
If one of the conditional probability is zero, then the entire expression becomes zero
Probability estimation:
mN
mpNCAP
cN
NCAP
N
NCAP
c
ici
c
ici
c
ici
)|(:estimate-m
1)|(:Laplace
)|( :Originalc: number of classes
p: prior probability
m: parameter
22Example
P(O)=4/10 P(Yes|O)=1/4 P(Single|O)=1/4 P(Mid|O)=1/4
Tid RefundMaritalStatus
TaxableIncome
Class
1 Yes Single High X
2 Yes Single High X
3 Yes Single High X
4 Yes Single High X
5 Yes Single High X
6 No Married High X
7 Yes Married Low O
8 No Single Low O
9 No Married Low O
10 No Married Mid O
P(X)=6/10 P(Yes|X)=5/6 P(Single|X)=5/6 P(Mid|X)=0
m
mXMidP
mN
mpNCAP
XMidPcN
NCAP
XMidPN
NCAP
c
ici
c
ici
c
ici
9
1.00)|(,)|(:estimate-m
9
1
36
10)|(,
1)|(:Laplace
6
0)|(,)|( :Original
23Laplace probability estimate
where k is the number of classes.
Problems with Laplace:Assumes all classes a priori equally likelyDegree of pruning depends on number of classes
kNnp C
C 1
24m-estimate of probability
pC = ( nC + pCa m ) / ( N + m)
where:
pCa = a prior probability of class C
m is a non-negative parameter tuned
by expert
25m-estimate
Important points: Takes into account prior probabilities Pruning not sensitive to number of classes Varying m: series of differently pruned
trees Choice of m depends on confidence in
data
26m-estimate in pruning
Choice of m:
Low noise low m little pruning
High noise high m much pruning
Note: Using m-estimate is as if examples at
node were a random sample, which they are
not. Suitably adjusting m compensates for this.
27貝氏信念網路 (Bayesian belief network , BBN)
28貝氏信念網路 範例 2- 已知高血壓
1967.08033.01)|(
8033.05185./49.*85.)(
)()|()|(
5185.051.2.49..85)()|()(
HighBPNoHDP
HighBPP
YesHDPYesHDHighBPPHighBPYesHDP
HDPHDHighBPPHighBPP
29貝氏信念網路 範例 3- 高血壓 ,健康飲食 ,規律運動
4138.05862.01),,|(
5862.075.*2.25.*85.
25.*85.
),|()|(
),|()|(
),|(),,(
),,|(
),,|(
YesEHealthyDHighBPNoHDP
YesEHealthyDHDPHDHighBPP
YesEHealthyDYesHDPYesHDHighBPP
YesEHealthyDYesHDPYesEHealthyDHighBPP
YesEHealthyDYesHDHighBPP
YesEHealthyDHighBPYesHDP
30
Artificial Neural Networks (ANN)
31Artificial Neural Networks (ANN)
X1 X2 X3 Y1 0 0 01 0 1 11 1 0 11 1 1 10 0 1 00 1 0 00 1 1 10 0 0 0
X1
X2
X3
Y
Black box
Output
Input
Output Y is 1 if at least two of the three inputs are equal to 1.
32Artificial Neural Networks (ANN)
X1 X2 X3 Y1 0 0 01 0 1 11 1 0 11 1 1 10 0 1 00 1 0 00 1 1 10 0 0 0
X1
X2
X3
Y
Black box
0.3
0.3
0.3 t=0.4
Outputnode
Inputnodes
otherwise0
trueis if1)( where
)04.03.03.03.0( 321
zzI
XXXIY
33Artificial 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
X1
X2
X3
Y
Black box
w1
t
Outputnode
Inputnodes
w2
w3
)( tXwIYi
ii Perceptron Model
)( tXwsignYi
ii
or
34General Structure of ANN
Activationfunction
g(Si )Si Oi
I1
I2
I3
wi1
wi2
wi3
Oi
Neuron iInput Output
threshold, t
InputLayer
HiddenLayer
OutputLayer
x1 x2 x3 x4 x5
y
Training ANN means learning the weights of the neurons
35Algorithm 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)
2),( i
iii XwfYE
36Support Vector Machines
Find a linear hyperplane (decision boundary) that will separate the data
37Support Vector Machines
One Possible Solution
B1
38Support Vector Machines
Another possible solution
B2
39Support Vector Machines
Other possible solutions
B2
40Support Vector Machines
Which one is better? B1 or B2? How do you define better?
B1
B2
41Support Vector Machines
Find hyperplane maximizes the margin => B1 is better than B2
B1
B2
b11
b12
b21
b22
margin
42Support Vector Machines
B1
b11
b12
0 bxw
1 bxw 1 bxw
1bxw if1
1bxw if1)(
xf 2||||
2 Margin
w
43Support 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)
2||||
2 Margin
w
1bxw if1
1bxw if1)(
i
i
ixf
2
||||)(
2wwL
44Support Vector Machines
What if the problem is not linearly separable?
45Support Vector Machines
What if the problem is not linearly separable?
– Introduce slack variables Need to minimize:
Subject to:
ii
ii
1bxw if1
-1bxw if1)(
ixf
N
i
kiC
wwL
1
2
2
||||)(
46Nonlinear Support Vector Machines
What if decision boundary is not linear?
47Nonlinear Support Vector Machines
Transform data into higher dimensional space
48Ensemble Methods
Construct a set of classifiers from the training data
Predict class label of previously unseen records by aggregating predictions made by multiple classifiers
49General Idea
OriginalTraining data
....D1D2 Dt-1 Dt
D
Step 1:Create Multiple
Data Sets
C1 C2 Ct -1 Ct
Step 2:Build Multiple
Classifiers
C*Step 3:
CombineClassifiers
50Why 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:
25
13
25 06.0)1(25
i
ii
i
51Examples of Ensemble Methods
How to generate an ensemble of classifiers?
– Bagging
– Boosting
52Bagging
Sampling with replacement
Build classifier on each bootstrap sample
Each sample has probability (1 – 1/n)n of being selected
Original Data 1 2 3 4 5 6 7 8 9 10Bagging (Round 1) 7 8 10 8 2 5 10 10 5 9Bagging (Round 2) 1 4 9 1 2 3 2 7 3 2Bagging (Round 3) 1 8 5 10 5 5 9 6 3 7
53Boosting
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
54Boosting
Records that are wrongly classified will have their weights increased
Records that are classified correctly will have their weights decreased
Original Data 1 2 3 4 5 6 7 8 9 10Boosting (Round 1) 7 3 2 8 7 9 4 10 6 3Boosting (Round 2) 5 4 9 4 2 5 1 7 4 2Boosting (Round 3) 4 4 8 10 4 5 4 6 3 4
• Example 4 is hard to classify
• Its weight is increased, therefore it is more likely to be chosen again in subsequent rounds
55Example: AdaBoost
Base classifiers: C1, C2, …, CT
Error rate:
Importance of a classifier:
N
jjjiji yxCw
N 1
)(1
i
ii
1ln
2
1
56Example: 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:
factor ionnormalizat theis where
)( ifexp
)( ifexp)()1(
j
iij
iij
j
jij
i
Z
yxC
yxC
Z
ww
j
j
T
jjj
yyxCxC
1
)(maxarg)(*
57
BoostingRound 1 + + + -- - - - - -
0.0094 0.0094 0.4623B1
= 1.9459
Illustrating AdaBoost
Data points for training
Initial weights for each data point
OriginalData + + + -- - - - + +
0.1 0.1 0.1
58Illustrating AdaBoost
BoostingRound 1 + + + -- - - - - -
BoostingRound 2 - - - -- - - - + +
BoostingRound 3 + + + ++ + + + + +
Overall + + + -- - - - + +
0.0094 0.0094 0.4623
0.3037 0.0009 0.0422
0.0276 0.1819 0.0038
B1
B2
B3
= 1.9459
= 2.9323
= 3.8744
59
Rule-Based Classifier