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Decision Tree Evolution using Limited number of Labeled Data Items from Drifting Data Streams Wei Fan 1, Yi-an Huang 2, and Philip S. Yu 1 1 IBM T.J.Watson 2 Georgia Tech

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Sad life cycle of inductive models Labeled Data Inductive Learner Inductive Model Decision Trees Rules Na ï ve Bayes Credit card transaction -> {fraud, normal} Un-labeled Real-time Streaming Data Predictions God knows the accuracy True Labels Accuracy too low!!!

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Seen any problems? Problem 1: we have no idea of the accuracy in the streaming environment. Problem 2: how long we can wait and how much we can afford to loose until we get labeled data?

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Solutions Solution I: error guessing and estimation. Idea 1: using observable statistical traits from the model itself to guess the error on unlabeled streaming data. Idea 2: using very small number of specifically acquired examples to statistically estimate the error – similar to estimate poll to estimate Bush or Kerry will win the presidency. Details: Active Mining of Data Streams by Wei Fan, Yi-an Huang, and Philip S. Yu appearing in SDM 04.

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Solutions Okay, assuming that we know that our model is too low in accuracy. Obviously, we need more accurate models. Solution II: We need to update our model with limited number of training examples We are interested in decision trees.

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Decision Tree Example A < 100 B < 50C < 34 Y N +: : 400 P(+|x) = 0.2 +: 90 - : 10 P(-|x) = 0.1 y N

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Class Distribution Replacement If a node is considered suspicious using one of our detection techniques, we can perform class distribution replacement. The idea is that:

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Class Distribution Replacement +: : 400 P(+|x) = 0.2 A < 100 B < 50C < 34 Y N +: 90 - : 10 P(-|x) = 0.1 y N Using limited number of examples, the new class distribution is P(+|x) = 0.4 P(+|x) = 0.4

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Some Statistics for Significance Test Proportion statistics: formula is in paper and many statistics books. Assume Gaussian distribution and compute significance

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Leaf Expansion Assume that significance test in leaf expansion fails. Solution: reconstruct the leaf using limited number of examples. Catch: not always possible. If the limited number of examples cannot justify an expansion, just keep the original node.

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Result on Class Distribution Replacement

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Result on Leaf Node Expansion

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More results in the paper Credit card fraud dataset. UCI Adult Dataset.

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Conclusion Pointed out the gap between data availability and pattern change. Proposed a general framework. Proposed a few methods to update and grow a decision tree from limited number of examples.

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