A Fully Distributed Framework A Fully Distributed Framework for Cost-sensitive Data Miningfor Cost-sensitive Data Mining

Wei Fan, Haixun Wang, and Philip S. YuWei Fan, Haixun Wang, and Philip S. YuIBM T.J.Watson, Hawthorne, New YorkIBM T.J.Watson, Hawthorne, New York

Salvatore J. StolfoSalvatore J. StolfoColumbia University, New York City, New YorkColumbia University, New York City, New York

Inductive LearningInductive Learning

TrainingData

Learner Classifier

($43.45,retail,10025,10040, ..., nonfraud)($246,70,weapon,10001,94583,...,fraud)

1. Decision trees2. Rules3. Naive Bayes...

Transaction {fraud,nonfraud}

TestData

($99.99,pharmacy,10013,10027,...,?)($1.00,gas,10040,00234,...,?)

Classifier Class Labels

nonfraudfraud

Distributed Data MiningDistributed Data Mining

ƒ data is inherently distributed across the network.many credit card authorization servers are distributed. Data are collected at each individual site.other examples include supermarket customer and transaction database, hotel reservations, travel agency and so on ...

ƒ In some situations, data cannot even be shared.many different banks have their data servers. They rather share the model but cannot share the data due to many reasons such as privacy, legal, and competitive reasons.

Cost-sensitive ProblemsCost-sensitive Problems

ƒ Charity Donation:Solicit to people who will donate large amount of charity.Costs $0.68 to send a letter.A(x): donation amount.Only solicit if A(x) > 0.68, otherwise lose money.

ƒ Credit card fraud detection:Detect frauds with high transaction amount

$90 to challenge a potential fraudA(x): fraudulant transaction amount.Only challenge if A(x) > $90, otherwise lose money.

Different Learning FrameworksDifferent Learning Frameworks

Fully Distributed Framework Fully Distributed Framework (training)(training)

D1 D2D2K sites

ML1ML2 MLt

C1 C2Ck

generate

K models

Fully-distributed Framework Fully-distributed Framework (predicting)(predicting)

DTest Set

C1 C2 Ck

Sent to k models

P1 P2 PkCompute k predictions

Combine

P

Combine to one prediction

Cost-sensitive Decision MakingCost-sensitive Decision Making

ƒ Assume that records the benefit received by predicting an example of class to be an instance of class .

ƒ The expected benefit received to predict an example to be an instance of class (regardless of its true label) is

ƒ The optimal decision-making policy chooses the label that maximizes the expected benefit, i.e.,

ƒ When and is a

traditional accuracy-based problem.ƒ Total benefits

Charity Donation ExampleCharity Donation Example

ƒ It costs $.68 to send a solicitation.ƒ Assume that is the best

estimate of the donation amount,

ƒ The cost-sensitive decision making will solicit an individual if and only if

Credit Card Fraud Detection Credit Card Fraud Detection ExampleExample

ƒ It costs $90 to challenge a potential fraud

ƒ Assume that y(x) is the transaction amount

ƒ The cost-sensitive decision making policy will predict a transaction to be fraudulent if and only if

Adult DatasetAdult Dataset

ƒ Downloaded from UCI database.ƒ Associate a benefit factor 2 to positives

and a benefit factor 1 to negatives

ƒ The decision to predict positive is

Calculating probabilitiesCalculating probabilities

For decision trees, n is the number of examples in a node and k is the number of examples with class label , then the probability is more sophisticated methods

smoothing:early stopping, and early stopping plus smoothing

For rules, probability is calucated in the same way as decision trees

For naive Bayes, is the score for

class label , then

binning

Combining Technique-Combining Technique-AveragingAveraging

ƒ Each model computes an expected benefit for example over every class label

ƒ Combining individual expected benefit together

ƒ We choose the label with the highest combined expected benefit

1. Decision threshold line2. Examples on the left are more profitable than those on the right3. "Evening effect": biases towards big fish.

Why accuracy is higher?Why accuracy is higher?

Partially distributed combining Partially distributed combining techniquestechniques

ƒ Regression:Treat base classifiers' outputs as indepedent variables of regression and the true label as dependent variables.

ƒ Modify Meta-learning:Learning a classifier that maps the base classifiers' class label predictions to that the true class label.For cost-sensitive learning, the top level classifier output probability instead of just a label.

Communication Overhead Communication Overhead SummarySummary

ExperimentsExperiments

ƒ Decision Tree Learner: C4.5 version 8ƒ Dataset:

Donation Credit CardAdult

Accuracy comparisionAccuracy comparision

Accuracy comparisonAccuracy comparison

Accuracy comparisonAccuracy comparison

Detailed SpreadDetailed Spread

Credit Card Fraud DatasetCredit Card Fraud Dataset

Adult DatasetAdult Dataset

Why accuracy is higher?Why accuracy is higher?

Summary and Future WorkSummary and Future Work

ƒ Evaluated a wide range of combining techniques include variations of averaging, regression and meta-learning for scalable cost-sensitive (and cost-insensitive learning).

ƒ Averaging, although simple, has the highest accuracy.

ƒ Previously proposed approaches have significantly more overhead and only work well for tradtional accuracy-based problems.

ƒ Future work: ensemble pruning and performance estimation