Main Reference PaperImproved randomized learning algorithms for imbalanced and noisy educational data classification, Computing, 2018 [Java/Python/R]
  • To improve the classification accuracy, a modification of the Randomized learner model is proposed to train the samples with exhibiting class imbalance and labeling errors. The proposed learning model uses a hybrid cost function that considering both robust and imbalanced data modeling objectives.

+ Description
  • To improve the classification accuracy, a modification of the Randomized learner model is proposed to train the samples with exhibiting class imbalance and labeling errors. The proposed learning model uses a hybrid cost function that considering both robust and imbalanced data modeling objectives.

  • To reduce the impacts of noisy data.

  • To improve the classification accuracy.

+ Aim & Objectives
  • To reduce the impacts of noisy data.

  • To improve the classification accuracy.

  • A different classifier is analyzed to check the feasibility and effectiveness of the proposal on a hybrid cost function.

+ Contribution
  • A different classifier is analyzed to check the feasibility and effectiveness of the proposal on a hybrid cost function.

  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

+ Software Tools & Technologies
  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

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

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

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

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

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