Main Reference Paper Detecting concept drift in data streams using model explanation, Applied Soft Computing, 2018 [Java]
  • A proposed concept drift detector is combined with an arbitrary classification algorithm. An Explanation Stream algorithm is periodically explains the incremental classifier and measures the dissimilarities in the stream of explanations. Since the attribute contribution values quantitatively describe the knowledge within the model and expect the measured changes in streaming data.

Description
  • A proposed concept drift detector is combined with an arbitrary classification algorithm. An Explanation Stream algorithm is periodically explains the incremental classifier and measures the dissimilarities in the stream of explanations. Since the attribute contribution values quantitatively describe the knowledge within the model and expect the measured changes in streaming data.

  • The concept drift is detected.

  • To improve the classification accuracy.

Aim & Objectives
  • The concept drift is detected.

  • To improve the classification accuracy.

  • The proposed work enhances the monitoring changes in the fractal dimension of explanation vectors to detect concept drift at various temporal granulations.

Contribution
  • The proposed work enhances the monitoring changes in the fractal dimension of explanation vectors to detect concept drift at various temporal granulations.

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Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

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Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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