Main Reference PaperClassification performance improvement using random subset feature selection algorithm for data mining,Big Data Research, 2018 [Python]
  • To improve the classification accuracy, a Random Subset Feature Selection algorithm is proposed to reduce the dimensionality of a data by selecting most important features. In every iteration, the training data set is modified by omitting the strong two features which are selected in the previous iteration. Then the data is classified using KNN classifier.

Description
  • To improve the classification accuracy, a Random Subset Feature Selection algorithm is proposed to reduce the dimensionality of a data by selecting most important features. In every iteration, the training data set is modified by omitting the strong two features which are selected in the previous iteration. Then the data is classified using KNN classifier.

  • To select the relevant features from high dimensional data.

  • To improve the classification accuracy

Aim & Objectives
  • To select the relevant features from high dimensional data.

  • To improve the classification accuracy

  • An efficient feature selection algorithm is contributed.

Contribution
  • An efficient feature selection algorithm is contributed.

  • 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.

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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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