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# ReverseTesting: An Efficient Framework to Select Amongst Classifiers under Sample Selection Bias - PowerPoint PPT Presentation

ReverseTesting: An Efficient Framework to Select Amongst Classifiers under Sample Selection Bias. Wei Fan IBM T.J.Watson Ian Davidson SUNY Albany. Sampling process. Where Sample Selection Bias Comes From?. Universe of Examples: Joint probability distribution P(x,y) = P(y|x) P(x)

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### ReverseTesting: An Efficient Framework to Select Amongst Classifiers under Sample Selection Bias

Wei Fan

IBM T.J.Watson

Ian Davidson

SUNY Albany

Sampling process Classifiers under

Where Sample Selection Bias Comes From?

Universe of Examples:

Joint probability

distribution

P(x,y) = P(y|x) P(x)

DM models this universe

Training

Data

Question:

Is the training data a good sample of the universe?

Algorithm

x

Model

y

Universe of Examples Classifiers under

Two classes:

red and green

red: f2>f1

green: f2<=f1

Unbiased & Biased Samples Classifiers under

Biased Sample:

less likely to sample points

close to decision boundary

Rather Unbiased Sample:

evenly distributed

Trained from Unbiased Sample Classifiers under

Trained from Biased Sample

Single Decision Tree

Error = 2.9%

Error = 7.9%

Trained from Unbiased Sample Classifiers under

Trained from Biased Sample

Random Decision Tree

Error = 3.1%

Error = 4.1%

What can we observe? Classifiers under

• Sample Selection Bias does affect modeling.

• Some techniques are more sensitive to bias than others.

• Models’ accuracy do get affected.

• One important question:

• How to choose amongst the best classification algorithm, given potentiallybiased dataset?

Ubiquitous Problem Classifiers under

• Fundamental assumption: training data is an unbiased sample from the universe of examples.

• Catalogue:

• Purchase history is normally only based on each merchant’s own data

• However, may not be representative of a population that may potentially purchase from the merchant..

• Drug Testing:

• Fraud Detection:

• Other examples (see Zadrozny’04 and Smith and Elkan’04)

Effect of Bias on Model Construction Classifiers under

• Inductive model:

• P(y|x,M): non-trivial dependency on the constructed model M.

• Recall that P(y|x) is the true conditional probability “independent” from any modeling techniques.

• In general, P(y|x,M) != P(y|x).

• If the model M is the “correct model”, sample selection bias doesn’t affect learning. (Fan,Davidson,Zadrozny, and Yu’05)

• Otherwise, it does.

• Key Issues:

• for real-world problems, we normally do not know the relationship between P(y|x,M) and P(y|x).

• No exact idea about where the bias comes from.

Re-Capping Our focus Classifiers under

• How to choose amongst the best classification algorithm, given potentiallybiased dataset?

• No information on the exactly how the data is biased

• No information on if the learners are affected by the bias.

• No information on true model, P(y|x)

Failure of Traditional Methods Classifiers under

• Given sample section bias, cross-validation based methods are a bad indicator of which methods are the most accurate.

• Results come next.

ReverseTesting Classifiers under

• Basic idea: how to use testing data’s feature vector x’s to help ordering different models even when their true labels y are not known.

MA Classifiers under

MBA

MAA

Labeled

test data

MBB

MB

MAB

A

A

DA

B

B

DB

Basic Procedure

Train

Test

Train

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

Rule Classifiers under

• 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.

Heuristics of ReverseTesting Classifiers under

• Assume that:

• A is more accurate than B

• Use both A and B labeled data to train two models.

• Using A’s data is likely to train a more accurate model than B’s data.

Result Summary Classifiers under

Why CV won Classifiers under ’t work?

Sparse Region

CV under-estimate in sparse regions Classifiers under

• 1. Examples in sparse regions are under represented in CV’s averaged results.

• Comparing those examples near the decision boundary

• A model performs badly in these under sample regions are not accurately

• estimated in cross-validation.

• 2. CV could also create “biased folds” in these “sparse” regions.

• Their estimate on biased region itself could also be unreliable.

• 3. No information on how a model behaves on “feature vectors” not represented in

• the training data.

Decision Boundary of one fold in 10-fold CV Classifiers under

1-fold

Full Training Data

Desiderata in ReverseTesting Classifiers under

• Not reduce the size of “sparse regions” as 10-fold CV does

• Not use “training model” or something close to training model.

• Utilize “feature vectors” not present in the training dataset.

C45 Decision Boundary Classifiers under

C45 can never learn such a model from training data

RDT labeled data

C45 labeled data

RDT Data

C45 labeled data

Training Data

RDT Decision Boundary Classifiers under

C45 labeled data

RDT labeled data

Model Comparison Classifiers under

• “Feature vectors in testing data” change the “decision boundary.

• The model constructed by algorithm A from A’s own labeled data != original “training model”.

• A’s “inductive bias” is represented in B’s space.

• “Use the changed boundary to include more emphasis on these sparse regions for both A and B re-trained on the two labeled test datasets.

Summary Classifiers under

• Sample Selection bias is a ubiquitous problem for DM and ML in practice.

• For most applications and modeling, techniques, sample selection bias does affect accuracy.

• Given sample selection bias, CV based method is bad at estimating order.

• ReverseTesting can do a much better job.

• Future work:

• not only orders but also estimates accuracy.