A bagging strategy-based kernel extreme learning machine for complex network intrusion detection

HIGHLIGHTS

• who: Shoulin Yin et al. from the Software College, Shenyang Normal University, Shenyang , China have published the article: A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection, in the Journal: (JOURNAL)
• what: The study shows that selective integration algorithm is superior to the signal Bagging or Boosting algorithm. The aim of this paper is to select an ensemble learner that is as small as possible but whose average eigenvector is as close as possible to a reference position in the first quadrant. In this paper, accuracy rate (AR) and missing rate . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.