Main Reference PaperDynamic weighted ensemble classification for credit scoring using Markov Chain, Applied Intelligence, 2018 [Java/Python/R]
  • A dynamic ensemble selection (DES) is proposed for credit scoring based on Markov Chain. A proposed Markov Chain model assigns a weight to each base classifier dynamically for each sample in the testing set. Then it combines each classifier’s classification result for each testing sample by using the dynamically weighted combiner.

+ Description
  • A dynamic ensemble selection (DES) is proposed for credit scoring based on Markov Chain. A proposed Markov Chain model assigns a weight to each base classifier dynamically for each sample in the testing set. Then it combines each classifier’s classification result for each testing sample by using the dynamically weighted combiner.

  • To improve the classification accuracy.

  • To implement the credit scoring system.

+ Aim & Objectives
  • To improve the classification accuracy.

  • To implement the credit scoring system.

  • A machine learning method has contributed to building the dynamically weighted combine ruler.

+ Contribution
  • A machine learning method has contributed to building the dynamically weighted combine ruler.

  • 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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