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# Sample Selection Bias - PowerPoint PPT Presentation

Sample Selection Bias – Covariate Shift: Problems, Solutions, and Applications. Wei Fan, IBM T.J.Watson Research Masashi Sugiyama, Tokyo Institute of Technology Updated PPT is available: http//www.weifan.info/tutorial.htm. Overview of Sample Selection Bias Problem. A Toy Example.

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### Sample Selection Bias – Covariate Shift: Problems, Solutions, and Applications

Wei Fan, IBM T.J.Watson Research

Masashi Sugiyama, Tokyo Institute of Technology

Updated PPT is available:

http//www.weifan.info/tutorial.htm

### Overview of Sample Selection Bias Problem Solutions, and Applications

A Toy Example Solutions, and Applications

Two classes:

red and green

red: f2>f1

green: f2<=f1

Unbiased and Biased Samples Solutions, and Applications

Not so-biased sampling

Biased sampling

Unbiased 96.9% Solutions, and Applications

Unbiased 97.1%

Unbiased 96.405%

Biased 95.9%

Biased 92.7%

Biased 92.1%

Effect on Learning

• Some techniques are more sensitive to bias than others.

• One important question:

• How to reduce the effect of sample selection bias?

Ubiquitous

• Loan Approval

• Drug screening

• Weather forecasting

• Fraud Detection

• User Profiling

• Biomedical Informatics

• Intrusion Detection

• Insurance

• etc

Face Recognition Solutions, and Applications

• Sample selection bias:

• Training samples are taken inside research lab, where there are a few women.

• Test samples: in real-world, men-women ratio is almost 50-50.

The Yale Face Database B

Brain-Computer Interface (BCI) Solutions, and Applications

• Control computers by EEG signals:

• Input: EEG signals

• Output: Left or Right

Figure provided by Fraunhofer FIRST, Berlin, Germany

Training Solutions, and Applications

• Imagine left/right-hand movement following the letter on the screen

Movie provided by Fraunhofer FIRST, Berlin, Germany

Testing: Playing Games Solutions, and Applications

• “Brain-Pong”

Movie provided by Fraunhofer FIRST, Berlin, Germany

Non-Stationarity in EEG Features Solutions, and Applications

• Different mental conditions (attention, sleepiness etc.) between training and test phases may change the EEG signals.

Bandpower differences between

training and test phases

Features extracted from brain activity

during training and test phases

Figures provided by Fraunhofer FIRST, Berlin, Germany

Robot Control Solutions, and Applicationsby Reinforcement Learning

• Let the robot learn how to autonomously move without explicit supervision.

Khepera Robot

Rewards Solutions, and Applications

Robot moves autonomously

= goes forward without hitting wall

• Give robot rewards:

• Go forward: Positive reward

• Hit wall: Negative reward

• Goal: Learn the control policy that maximizes future rewards

Example Solutions, and Applications

• After learning:

Policy Iteration and Covariate Shift Solutions, and Applications

• Policy iteration:

• Updating the policy correspond to changing the input distributions!

Evaluate

control policy

Improve

control policy

### Different Types of Sample Selection Bias Solutions, and Applications

Bias as Distribution Solutions, and Applications

• Think of “sampling an example (x,y) into the training data” as an event denoted by random variable s

• s=1: example (x,y) is sampled into the training data

• s=0: example (x,y) is not sampled.

• Think of bias as a conditional probability of “s=1” dependent on x and y

• P(s=1|x,y) : the probability for (x,y) to be sampled into the training data, conditional on the example’s feature vector x and class label y.

Categorization Solutions, and Applications(Zadrozy’04, Fan et al’05, Fan and Davidson’07)

• No Sample Selection Bias

• P(s=1|x,y) = P(s=1)

• Feature Bias/Covariate Shift

• P(s=1|x,y) = P(s=1|x)

• Class Bias

• P(s=1|x,y) = P(s=1|y)

• Complete Bias

• No more reduction

Bias for a Training Set Solutions, and Applications

• How P(s=1|x,y) is computed

• Practically, for a given training set D

• P(s=1|x,y) = 1: if (x,y) is sampled into D

• P(s=1|x,y) = 0: otherwise

• Alternatively, consider D of the size can be sampled “exhaustively” from the universe of examples.

Realistic Datasets are biased? Solutions, and Applications

• Most datasets are biased.

• Unlikely to sample each and every feature vector.

• For most problems, it is at least feature bias.

• P(s=1|x,y) = P(s=1|x)

Effect on Learning Solutions, and Applications

• Learning algorithms estimate the “true conditional probability”

• True probability P(y|x), such as P(fraud|x)?

• Estimated probabilty P(y|x,M): M is the model built.

• Conditional probability in the biased data.

• P(y|x,s=1)

• Key Issue:

• P(y|x,s=1) = P(y|x) ?

### Bias Resolutions Solutions, and Applications

Heckman’s Two-Step Approach Solutions, and Applications

• Estimate one’s donation amount if one does donate.

• Accurate estimate cannot be obtained by a regression using only data from donors.

• First Step: Probit model to estimate probability to donate:

• Second Step: regression model to estimate donation:

• Expected error

• Gaussian assumption

Covariate Shift or Feature Bias Solutions, and Applications

• However, no chance for generalization if training and test samples have nothing in common.

• Covariate shift:

• Input distribution changes

• Functional relation remains unchanged

Example of Covariate Shift Solutions, and Applications

(Weak) extrapolation:

Predict output values outside training region

Training samples

Test samples

Covariate Shift Adaptation Solutions, and Applications

• To illustrate the effect of covariate shift, let’s focus on linear extrapolation

Training samples

Test samples

True function

Learned function

Generalization Error Solutions, and Applications= Bias + Variance

: expectation over noise

Model Specification Solutions, and Applications

• Model is said to be correctly specified if

• In practice, our model may not be correct.

• Therefore, we need a theory for misspecified models!

If model is correct: Solutions, and Applications

OLS minimizes bias asymptotically

If model is misspecified:

OLS does not minimize bias even asymptotically.

We want to reduce bias!

Ordinary Least-Squares (OLS)

Law of Large Numbers Solutions, and Applications

• Sample average converges to the population mean:

• We want to estimate the expectation overtest input points only using training input points .

Key Trick: Solutions, and ApplicationsImportance-Weighted Average

• Importance: Ratio of test and training input densities

• Importance-weighted average:

(cf. importance sampling)

Even for misspedified models, IWLS Solutions, and Applicationsminimizes bias asymptotically.

We need to estimate importance in practice.

Importance-Weighted LS

(Shimodaira, JSPI2000)

:Assumed strictly positive

Use of Unlabeled Samples: Importance Estimation Solutions, and Applications

• Assumption: We have training inputs and test inputs .

• Naïve approach: Estimate and separately, and take the ratio of the density estimates

• This does not work well since density estimation is hard in high dimensions.

Vapnik’s Principle Solutions, and Applications

When solving a problem,

more difficult problems shouldn’t be solved.

• Directly estimating the ratio is easier than estimating the densities!

(e.g., support vector machines)

Knowing densities

Knowing ratio

Modeling Importance Function Solutions, and Applications

• Use a linear importance model:

• Test density is approximated by

• Idea: Learn so that well approximates .

Kullback-Leibler Divergence Solutions, and Applications

(constant)

(relevant)

Learning Importance Function Solutions, and Applications

• Thus

• Since is density,

(objective function)

(constraint)

KLIEP (Kullback-Leibler Solutions, and ApplicationsImportance Estimation Procedure)

(Sugiyama et al., NIPS2007)

• Convexity: unique global solution is available

• Sparse solution: prediction is fast!

Examples Solutions, and Applications

Experiments: Setup Solutions, and Applications

• Input distributions: standard Gaussian with

• Training: mean (0,0,…,0)

• Test: mean (1,0,…,0)

• Kernel density estimation (KDE):

• Separately estimate training and test input densities.

• Gaussian kernel width is chosen by likelihood cross-validation.

• KLIEP

• Gaussian kernel width is chosen by likelihood cross-validation

Experimental Results Solutions, and Applications

• KDE:Error increases as dim grows

• KLIEP: Error remains small for large dim

KDE

Normalized MSE

KLIEP

dim

Ensemble Methods (Fan and Davidson’07) Solutions, and Applications

Averaging of estimated class probabilities weighted by posterior

Posterior

weighting

Integration Over

Model Space

Class

Probability

Removes model uncertainty by averaging

How to Use Them Solutions, and Applications

• Estimate “joint probability” P(x,y) instead of just conditional probability, i.e.,

• P(x,y) = P(y|x)P(x)

• Makes no difference use 1 model, but

Multiple models

Examples of How This Works Solutions, and Applications

• P1(+|x) = 0.8 and P2(+|x) = 0.4

• P1(-|x) = 0.2 and P2(-|x) = 0.6

• model averaging,

• P(+|x) = (0.8 + 0.4) / 2 = 0.6

• P(-|x) = (0.2 + 0.6)/2 = 0.4

• Prediction will be –

• But if there are two P(x) models, with probability 0.05 and 0.4

• Then

• P(+,x) = 0.05 * 0.8 + 0.4 * 0.4 = 0.2

• P(-,x) = 0.05 * 0.2 + 0.4 * 0.6 = 0.25

• Recall with model averaging:

• P(+|x) = 0.6 and P(-|x)=0.4

• Prediction is +

• But, now the prediction will be – instead of +

• Key Idea:

• Unlabeled examples can be used as “weights” to re-weight the models.

Structural Discovery

Original Dataset

Structural Re-balancing

Corrected Dataset

• Quality of learned functions depends on training input location .

• Goal: optimize training input location

Good input location

Poor input location

Target

Learned

Challenges 0.4

• Generalization error is unknown and needs to be estimated.

• In experiment design, we do not have training output valuesyet.

• Thus we cannot use, e.g., cross-validationwhich requires .

• Only training input positions can be used in generalization error estimation!

• The model is not correctin practice.

• Then OLS is not consistent.

• Standard “experiment design” method does not work!

(Fedorov 1972; Cohn et al., JAIR1996)

Bias Reduction by 0.4Importance-Weighted LS (IWLS)

(Wiens JSPI2001; Kanamori & Shimodaira JSPI2003; Sugiyama JMLR2006)

• The use of IWLS mitigates the problem of in consistency under agnostic setup.

• Importance is known in active learning setup since is designed by us!

Importance

### Model Selection and Testing 0.4

Polynomial of order 1

Polynomial of order 2

Polynomial of order 3

• Choice of models is crucial:

• We want to determine the model so that generalization error is minimized:

• Generalization error is not accessible since the target function is unknown.

• Instead, we use a generalization error estimate.

Model complexity

Model complexity

• Divide training samples into groups.

• Train a learning machine with groups.

• Validate the trained machine using the rest.

• Repeat this for all combinations and output the mean validation error.

• CV is almost unbiased without covariate shift.

• But, itis heavily biased under covariate shift!

Group 1

Group 2

Group k-1

Group k

Training

Validation

(Zadrozny ICML2004; Sugiyama et al., JMLR2007)

• When testing the classifier in CV process, we also importance-weight the test error.

• IWCV gives almost unbiased estimates of generalization error even under covariate shift

Set 1

Set 2

Set k-1

Set k

Training

Testing

• IWCV gives better estimates of generalization error.

• Model selection by IWCV outperforms CV!

MA 0.4

MBA

MAA

Labeled

test data

MBB

MB

MAB

A

A

DA

B

B

DB

Reserve Testing (Fan and Davidson’06)

Train

Test

Train

Estimate the performance of MA and MB based on the order of MAA, MAB, MBA and MBB

Rule 0.4

• If “A’s labeled test data” can construct “more accurate models” for both algorithm A and B evaluated on labeled training data, then A is expected to be more accurate.

• If MAA > MAB and MBA > MBB then choose A

• Similarly,

• If MAA < MAB and MBA < MBB then choose B

• Otherwise, undecided.

Sparse Region

### Examples 0.4

• Daily summary maps of two datasets from Texas Commission on Environmental Quality (TCEQ)

• Rather skewed and relatively sparse distribution

• 2500+ examples over 7 years (1998-2004)

• 72 continuous features with missing values

• Large instance space

• If binary and uncorrelated, 272 is an astronomical number

• 2% and 5% true positive ozone days for 1-hour and 8-hour peak respectively

• A large number of irrelevant features 0.4

• Only about 10 out of 72 features verified to be relevant,

• No information on the relevancy of the other 62 features

• For stochastic problem, given irrelevant features Xir , where X=(Xr, Xir),

P(Y|X) = P(Y|Xr) only if the data is exhaustive.

• May introduce overfitting problem, and change the probability distribution represented in the data.

• P(Y = “ozone day”| Xr, Xir) 1

• P(Y = “normal day”|Xr, Xir) 0

1 0.4

1

2

+

2

+

+

+

-

3

3

-

+

+

Testing Distribution

Training Distribution

• “Feature sample selection bias”.

• Given 7 years of data and 72 continuous features, hard to find many days in the training data that is very similar to a day in the future

• Given these, 2 closely-related challenges

• How to train an accurate model

• How to effectively use a model to predict the future with a different and yet unknown distribution

Reliable probability estimation under 0.4irrelevant features

• Recall that due to irrelevant features:

• P(Y = “ozone day”| Xr, Xir) 1

• P(Y = “normal day”|Xr, Xir) 0

• Construct multiple models

• Average their predictions

• P(“ozone”|xr): true probability

• P(“ozone”|Xr, Xir, θ): estimated probability by model θ

• MSEsinglemodel:

• Difference between “true” and “estimated”.

• MSEAverage

• Difference between “true” and “average of many models”

• Formally show that MSEAverage ≤ MSESingleModel

Ma 0.4

Mb

VE

Precision

Estimated

probability

values

1 fold

Estimated

probability

values

10 fold

Estimated

probability

values

2 fold

Concatenate

10CV

Recall

“Probability-

TrueLabel”

file

PrecRec

plot

Decision

threshold

VE

TrainingSet Algorithm

…..

10CV

Concatenate

1

1

2

+

2

+

+

+

P(y=“ozoneday”|x,θ) Lable

7/1/98 0.1316 Normal

7/3/98 0.5944 Ozone

7/2/98 0.6245 Ozone

………

P(y=“ozoneday”|x,θ) Lable

7/1/98 0.1316 Normal

7/2/98 0.6245 Ozone

7/3/98 0.5944 Ozone

………

-

3

3

-

+

+

Testing Distribution

Training Distribution

• A CV based procedure for decision threshold selection

• Prediction with feature sample selection bias

Whole TrainingSet

if P(Y = “ozonedays”|X,θ ) ≥ VE

θ

Predict “ozonedays”

• Prediction with feature sample selection bias

• Future prediction based on decision threshold selected

Results 0.4

• Task 1: Who rated what in 2006

• Given a list of 100,000 pairs of users and movies, predict for each pair the probability that the user rated the movie in 2006

• Result: They are the close runner-up, No 3 out of 39 teams

• Challenges:

• Huge amount of data how to sample the data so that any learning algorithms can be applied is critical

• Complex affecting factors: decrease of interest in old movies, growing tendency of watching (reviewing) more movies by Netflix users

NO User

or Movie

Arrival

User Arrival

Movie Arrival

17K movies

Training Data

1998 Time 2005 2006

Qualifier

Dataset

3M

…… 0.4

Movie5 .0011

……

Movie3 .001

……

Movie4 .0007

….

1488844,3,2005-09-06

822109,5,2005-05-13

885013,4,2005-10-19

30878,4,2005-12-26

823519,3,2004-05-03

……

Movie5 User 7

……

Movie3 User 7

……

Movie4 .User 8

……

User7 .0007

……

User6 .00012

……

User8 .00003

……

• Sampling the movie-user pairs for “existing” users and “existing” movies from 2004, 2005 as training set and 4Q 2005 as developing set

• The probability of picking a movie was proportional to the number of ratings that movie received; the same strategy for users

Movies

Samples

History

Users

• Learning Algorithm: 0.4

• Single classifiers: logistic regression, Ridge regression, decision tree, support vector machines

• Naïve Ensemble: combining sub-classifiers built on different types of features with pre-set weights

• Ensemble classifiers: combining sub-classifiers with weights learned from the development set

• Control computers by brain signals:

• Input: EEG signals

• Output: Left or Right

BCI Results 0.4

KL divergence from training

to test input distributions

• When KL is large, covariate shift adaptation tends to improve accuracy.

• When KL is small, no difference.

Robot Control by 0.4Reinforcement Learning

• Swing-up inverted pendulum:

• Swing-up the pole by controlling the car.

• Reward:

Results 0.4

Existing method (b)

Existing method (a)

Wafer Alignment in 0.4Semiconductor Exposure Apparatus

• Recent silicon wafers have layer structure.

• Circuit patterns are exposed multiple times.

• Exact alignment of wafers is very important.

• Wafer alignment process:

• Measure marker location printed on wafers.

• Shift and rotate the wafer to minimize the gap.

• For speeding up, reducing the number of markers to measure is very important.

Active learning problem!

• When gap is only shift and rotation, linear model is exact:

• However, non-linear factors exist, e.g.,

• Warp

• Biased characteristic of measurement apparatus

• Different temperature conditions

• Exactly modeling non-linear factors is very difficult in practice!

Agnostic setup!

(Sugiyama & Nakajima ECML-PKDD2008)

Mean squared error of wafer position estimation

• IWLS-based active learning works very well!

• 20 markers (out of 38) are chosen by experiment design methods.

• Gaps of all markers are predicted.

• Repeated for 220 different wafers.

• Mean (standard deviation) of the gap prediction error

• Red: Significantly better by 5% Wilcoxon test

• Blue: Worse than the baseline passive method

• Quiñonero-Candela, Sugiyama, Schwaighofer & Lawrence (Eds.), Dataset Shift in Machine Learning, MIT Press, Cambridge, 2008.