Discussion of article "Deep Neural Networks (Part VIII). Increasing the classification quality of bagging ensembles"

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MetaQuotes Software Corp.
Moderator
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MetaQuotes Software Corp.  

New article Deep Neural Networks (Part VIII). Increasing the classification quality of bagging ensembles has been published:

The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated.

The figure below provides a simplified scheme of all calculations: it shows the stages, the used scripts and data structures.


Fig. 11. Structure and sequence of the main calculations in the article.

Author: Vladimir Perervenko

Vladimir Perervenko
4717
Vladimir Perervenko  

Discussion and questions on the code can be done in the branch

Good luck

geraldkibz
5
geraldkibz  
After choosing the best 7 ensembles and classifying it[=1,0,1] ,I would like to extract the data to train it on a Keras model, but I can't seem to find the specific dataframes.
Vladimir Perervenko
4717
Vladimir Perervenko  
geraldkibz:
After choosing the best 7 ensembles and classifying it[=1,0,1] ,I would like to extract the data to train it on a Keras model, but I can't seem to find the specific dataframes.

Figure 11 shows the structural scheme of calculations. Above each stage is the name of the script. Under each stage is the name of the resulting data structure. What data do you want to use?

Vladimir Perervenko
4717
Vladimir Perervenko  

If you want to use the averaged continuous predictions of the seven best ensembles, then they are in the structure

testX1[[k]]$TrainYpred[ ,j]

  k = c(origin/repaired/removed/relabeled)

 j = c( half, mean, med, both)  

If you need the predictions of the seven best in binary form, then they are in the structure

VotAver[[k]]Train.clVoting[1001,j]
VotAver[[k]]Test.clVoting[501,j]
VotAver[[k]]Test1.clVoting[251,j]
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