e nsemble l earning : a da b oost jianping fan dept of computer science unc-charlotte

ENSEMBLE LEARNING: ADABOOST

Jianping Fan Dept of Computer Science UNC-Charlotte

ENSEMBLE LEARNINGA machine learning paradigm where multiple learners are used to solve the problem

Problem

… ...… ...

Problem

Learner Learner Learner Learner

Previously:

Ensemble:

The generalization ability of the ensemble is usually significantly better than that of an individual learner

Boosting is one of the most important families of ensemble methods

3

Bootstrapping

Bagging

Boosting (Schapire 1989)

Adaboost (Schapire 1995)

A BRIEF HISTORY Resampling for estimating statistic

Resampling for classifier design

BOOTSTRAP ESTIMATION

Repeatedly draw n samples from D

For each set of samples, estimate a statistic

The bootstrap estimate is the mean of the individual estimates

Used to estimate a statistic (parameter) and its variance

BAGGING - AGGREGATE BOOTSTRAPPING

For i = 1 .. MDraw n*<n samples from D with

replacementLearn classifier Ci

Final classifier is a vote of C1 .. CM

Increases classifier stability/reduces variance

BAGGING

f1

f2

fT

ML

ML

ML

f

Random sa

mple

with re

placem

ent

Random sample

with replacement

BOOSTING

Training Sample

Weighted Sample

Weighted Sample

fT

f1

…

f2

f

ML

ML

ML

REVISIT BAGGING

BOOSTING CLASSIFIER

BAGGING VS BOOSTING

Bagging: the construction of complementary base-learners is left to chance and to the unstability of the learning methods.

Boosting: actively seek to generate complementary base-learner--- training the next base-learner based on the mistakes of the previous learners.

BOOSTING (SCHAPIRE 1989)BOOSTING (SCHAPIRE 1989) Randomly select n1 < n samples from D without

replacement to obtain D1Train weak learner C1

Select n2 < n samples from D with half of the samples misclassified by C1 to obtain D2Train weak learner C2

Select all samples from D that C1 and C2 disagree onTrain weak learner C3

Final classifier is vote of weak learners

ADABOOST (SCHAPIRE ADABOOST (SCHAPIRE 1995)1995)Instead of sampling, re-weight

Previous weak learner has only 50% accuracy over new distribution

Can be used to learn weak classifiers

Final classification based on weighted vote of weak classifiers

ADABOOST TERMSLearner = Hypothesis = Classifier

Weak Learner: < 50% error over any distribution

Strong Classifier: thresholded linear combination of weak learner outputs

AdaBoostAdaptive

A learning algorithm

Building a strong classifier a lot of weaker ones

Boosting

ADABOOST CONCEPT

1 { 1 }) , 1(h x

.

.

.

weak classifiers

slightly better than random

1

( )( )T

ttT th xH x sign

2 { 1 }) , 1(h x

{ 1( }) , 1Th x

strong classifier

WEAKER CLASSIFIERS

1 { 1 }) , 1(h x

.

.

.

weak classifiers


1

( )( )T

ttT th xH x sign

2 { 1 }) , 1(h x

{ 1( }) , 1Th x

strong classifier

Each weak classifier learns by considering one simple feature

T most beneficial features for classification should be selected

How to– define features?– select beneficial features?– train weak classifiers?– manage (weight) training

samples?– associate weight to each weak

classifier?

THE STRONG CLASSIFIERS

1 { 1 }) , 1(h x

.

.

.

weak classifiers


1

( )( )T

ttT th xH x sign

2 { 1 }) , 1(h x

{ 1( }) , 1Th x

strong classifier

How good the strong one will be?How good the strong one will be?

THE ADABOOST ALGORITHM

Given: 1 1 where ( , ), , ( , ) , { 1, 1}m m i ix y x y x X y

Initialization: 1

1( ) , 1, ,mD i i m

For :1, ,t T

• Find classifier which minimizes error wrt Dt ,i.e., : { 1, 1}th X

1

where arg min ( )[ ( )]j

m

t j j t i j iih

h D i y h x

:probability distribution of 's at time ( )t iD i x t:probability distribution of 's at time ( )t iD i x t

• Weight classifier: 11

ln2

tt

t

• Update distribution: 1

( ) exp[ ( )], is for normalizati( ) ont t i t i

t tt

D i y h xD i Z

Z

minimize weighted errorminimize weighted error

for minimize exponential lossfor minimize exponential loss

Give error classified patterns more chance for learning.Give error classified patterns more chance for learning.



Initialization: 1

1( ) , 1, ,mD i i m

For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z

Output final classifier:1

( ) ( )T

t tt

sign H x h x

BOOSTING ILLUSTRATION

Weak

Classifier 1


Weights

Increased


typicallywhere

the weights of incorrectly classified examples are increased so that the base learner is forced to focus on the hard examples in the training set

where


Weak

Classifier 2


Weights

Increased


Weak

Classifier 3


Final classifier is

a combination of weak classifiers



Initialization: 1

1( ) , 1, ,mD i i m

For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x

What goal the AdaBoost wants to reach?



Initialization: 1

1( ) , 1, ,mD i i m

For :1, ,t T


1


m

t j j t i j iih

h D i y h x

• Weight classifier:



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x

What goal the AdaBoost wants to reach?

11ln

2t

tt

They are goal dependent.They are goal dependent.

GOAL

Minimize exponential loss

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ,( ) yH x

x yloss H x E e

GOAL

Minimize exponential loss

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ,( ) yH x

x yloss H x E e ( )yH x

Maximize the margin yH(x)

GOALFinal classifier:

1

( ) ( )T

t tt

sign H x h x

( )exp ,( ) yH x

x yloss H x E e Minimize

( ) ( ), |t tyH x yH x

x y x yE e E E e x

Define 1( ) ( ) ( )t t t tH x H x h x with 0 ( ) 0H x

Then, ( ) ( )TH x H x

1[ ( ) ( )] |t t ty H x h xx yE E e x

1 ( ) ( ) |t t tyH x y h xx yE E e e x

1 ( ) ( ( )) ( ( ))t t tyH xx t tE e e P y h x e P y h x

( ) ( ), |t tyH x yH x

x y x yE e E E e x

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ,( ) yH x

x yloss H x E e Minimize



1 ( ) ( ( )) ( ( ))t t tyH xx t tE e e P y h x e P y h x

( ), 0tyH x

x yt

E e

Set

1 ( ) ( ( )) ( ( )) 0t t tyH xx t tE e e P y h x e P y h x

0

?t

Final classifier:1

( ) ( )T

t tt

sign H x h x

Minimize




0

( ( ))1ln

2 ( ( ))t

tt

P y h x

P y h x

11

ln2

tt

t

(error)t P

?t ( )

exp ,( ) yH xx yloss H x E e

( , ) ( )i i tP x y D i( , ) ( )i i tP x y D i

1

( )[ ( )]m

t i j ii

D i y h x

( )exp ,( ) yH x

x yloss H x E e

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

Minimize

Define 1( ) ( ) ( )t t t tH x H x h x 0 ( ) 0H x



0

( ( ))1ln

2 ( ( ))t

tt

P y h x

P y h x

11

ln2

tt

t

?t Given: 1 1 where ( , ), , ( , ) , { 1, 1}m m i ix y x y x X y

I nitialization: 11( ) , 1, ,mD i i m

For :1, ,t T

•Find classifier which minimizes error wrt Dt ,i.e.,: { 1, 1}th X

1


m

t j j t i j iih

h D i y h x

•Weight classifier:11

ln2

tt

t

•Update distribution: 1


t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x



For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x

(error)t P


1

( )[ ( )]m

t i j ii

D i y h x

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ~ ,( ) yH x

x D yloss H x E e Minimize




0

( ( ))1ln

2 ( ( ))t

tt

P y h x

P y h x

11

ln2

tt

t

1 ?tD



For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x



For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x

(error)t P


1

( )[ ( )]m

t i j ii

D i y h x

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ~ ,( ) yH x




1 ?tD

1, ,

t t t tyH yH y hx y x yE e E e e

1 2 2 2,

11

2tyH

x y t t t tE e y h y h

1 2 2 2,

1arg min 1

2tyH

t x y t th

h E e y h y h

2 2 1y h

1 2,

1arg min 1

2tyH

t x y t th

h E e y h

1 21arg min 1 |

2tyH

t x y t th

h E E e y h x

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ~ ,( ) yH x




1 ?tD

1 21arg min 1 |

2tyH

t x y t th

h E E e y h x

1arg min |tyHt x y t

hh E E e y h x

1arg max |tyHt x y

hh E E e yh x

1 1( ) ( )arg max 1 ( ) ( 1| ) ( 1) ( ) ( 1| )t tH x H xt x

hh E h x e P y x h x e P y x

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ~ ,( ) yH x




1 ?tD

( )1, ~ ( | )arg max ( )yH xtt x y e P y xh

h E yh x maximized when ( ) y h x x

( ) ( )1 1, ~ ( | ) , ~ ( | )( ) ( 1| ) ( 1| )yH x yH xt tt x y e P y x x y e P y x

h x sign P y x P y x

( )1, ~ ( | )( ) |yH xtt x y e P y x

h x sign E y x

1 1( ) ( )arg max 1 ( ) ( 1| ) ( 1) ( ) ( 1| )t tH x H xt x

hh E h x e P y x h x e P y x

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ~ ,( ) yH x




1 ?tD

( ) ( )1 1, ~ ( | ) , ~ ( | )( ) ( 1| ) ( 1| )yH x yH xt tt x y e P y x x y e P y x

h x sign P y x P y x

1 ( ), ~ ( | )tyH xx y e P y xAt time t

with

Final classifier:1

( ) ( )T

t tt

sign H x h x

( )exp ~ ,( ) yH x




1 ?tD

1 ( ), ~ ( | )tyH xx y e P y xAt time t

At time 1



For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x



For :1, ,t T


1


m

t j j t i j iih

h D i y h x


ln2

tt

t



t tt

D i y h xD i Z

Z


( ) ( )T

t tt

sign H x h x

, ~ ( | )x y P y x ( | ) 1i iP y x 11

1 1(1)D

Z m

At time t+1( ), ~ ( | )tyH xx y e P y x ( )t tyh x

tD e

1

( ) exp[ ( ), is for normaliza i

]( ) t ont t i t i

t tt

D i y h xD i Z

Z

41

PROS AND CONS OF ADABOOST

AdvantagesVery simple to implementDoes feature selection resulting

in relatively simple classifierFairly good generalization

DisadvantagesSuboptimal solutionSensitive to noisy data and

outliers

INTUITION

Train a set of weak hypotheses: h1, …., hT.

The combined hypothesis H is a weighted majority vote of the T weak hypotheses. Each hypothesis ht has a weight αt.

During the training, focus on the examples that are misclassified. At round t, example xi has the weight Dt(i).

BASIC SETTING Binary classification problem Training data:

Dt(i): the weight of xi at round t. D1(i)=1/m.

A learner L that finds a weak hypothesis ht: X Y given the training set and Dt

The error of a weak hypothesis ht:

}1,1{,),,(),....,,( 11 YyXxwhereyxyx iimm

THE BASIC ADABOOST ALGORITHM

For t=1, …, T

• Train weak learner using training data and Dt

• Get ht: X {-1,1} with error

• Choose

• Update

iit yxhitt iD

)(:

)(

t

tt

1ln

2

1

t

xhyt

iit

iit

t

tt

Z

eiD

yxhife

yxhife

Z

iDiD

itit

t

t

)(

1

)(

)(

)(*

)()(

THE GENERAL ADABOOST ALGORITHM

46

PROS AND CONS OF ADABOOST

AdvantagesVery simple to implementDoes feature selection resulting

in relatively simple classifierFairly good generalization

DisadvantagesSuboptimal solutionSensitive to noisy data and

outliers

e nsemble l earning : a da b oost jianping fan dept of computer science unc-charlotte

Documents

weight classifier

distributionstrong classifier

weak learnersadaboost

train weak classifiers

d2train weak learner

reweightprevious weak

output final classifier

classifierweak learner