On reducing classifier granularity in mining concept drifting data streams
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On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams. Peng Wang, H. Wang, X. Wu, W. Wang, and B. Shi Proc. of the Fifth IEEE International Conference on Data Mining (ICDM ’ 05). Speaker: Yu Jiun Liu Date : 2006/9/26. Introduction. State of the art

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On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams

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On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams

Peng Wang, H. Wang, X. Wu, W. Wang, and B. Shi

Proc. of the Fifth IEEE International Conference on Data Mining (ICDM’05)

Speaker: Yu Jiun Liu

Date : 2006/9/26


Introduction

  • State of the art

    • The incrementally updated classifiers.

    • The ensemble classifiers.

  • Model Granularity

    • Traditional : monolithic

    • This paper : semantic decomposition


Motivation

  • The model is decomposable into smaller components.

  • The decomposition is semantic-aware in the sense.


Monolithic Models

  • Stream :

  • Attributes :

  • Class Label :

  • Window :

  • Model (Classifier) :Ci


Rule-based Models

  • A rule form :

  • minsup = 0.3 and minconf = 0.8

  • Valid rules of W1 are:

  • Valid rules of W3 are:


Algorithm

  • Phase 1 : Initialization

    • Use the first w records to train all valid rules for window W1.

    • Construct the RS-tree and REC-tree.

  • Phase 2 : Update

    • When record arrives, insert it into the REC-tree and update the sup. and conf. of the rules matched by it.

    • Delete oldest record and update the value matched by it.


Data Structure


RS-Tree

  • A prefix tree with attribute order

  • Each node N represents a unique rule R : P  Ci

  • N’ (P’  Cj) is a child node of N, iff:


REC-Tree

  • Each record r as a sequence

  • Node N points to rule

    in the RS-tree if :


Detecting Concept Drifts

  • percentage V.S. the distribution of the misclassified records.

The percentage approach cannot tell us which part of the classifier gives rise to the inaccuracy.


Definition


Finding Rule Algorithm


Update Algorithm


Experiments

  • CPU : 1.7 GHz

  • Memory : 256MB

  • Datasets : synthetic and real life dataset.

    • Synthetic :

    • Real life dataset :

      • 10,344 recodes and 8 dimensions.


Synthetic

10 dimensions

Window size 5000

4 dimensions changing

Effect of model updating


The relation of concept drifts and


Effect of rule composition


Accuracy and Time

  • Window size : 10,000

  • EC : 10 classifiers, each trained on 1000 records.

  • Synthetic data.


Real life data


Conclusion

  • Overcome the effects of concept drifts.

  • By reducing granularity, change detection and model update can be more efficient without compromising classification accuracy.


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