+(91) 99007-25045
support@evolettechnologies.com
Booster in High Dimensional Data Classification
  • Booster in High Dimensional Data Classification

Booster in High Dimensional Data Classification

Sold By Final year Student Project
Categories: , Tags: , ,
  • Description
  • Reviews (0)

Product Description

Booster High Dimensional Data Classification Final Year IEEE Projects

Due to small number of observations in high dimensional data, the classification problems are becoming more common especially in microarray data.Booster High Dimensional Data Classification Final Year IEEE Projects Lots of efficient classification models and feature selection (FS) algorithms have been proposed during the last two decades for higher prediction accuracies.

However, the result of an FS algorithm based on the prediction accuracy will be unstable over the variations in the training set, especially in high dimensional data.Booster High Dimensional Data Classification Final Year IEEE Projects. The proposed system provides a new evaluation measure Q-statistic that incorporates the stability of the selected feature subset in addition to the prediction accuracy.

The system also proposes a Booster of an FS algorithm that boosts the value of the Q-statistic of the algorithm applied. Empirical studies based on synthetic data and 14 microarray data sets show that Booster boosts not only the value of the Q-statistic but also the prediction accuracy of the algorithm applied unless the data set is intrinsically difficult to predict with the given algorithm

Reviews

There are no reviews yet. Add a review