I want to use DBN to reduce the 41 features of nslkdd dataset after transforming nominal data to numeric the number of features increases from 41 to 121 . I used 3 RBMs (121-50-10) now I want to know the 10 selected features i.e know their names to put them as an input to the classifier. how can I do it?
Topic keras tensorflow rbm deep-learning python
Category Data Science
In general you are extracting/creating 10 features that scan theoretically recreate the input i.e. the 41 features. The features on their own may not necessarily make a lot of sense and (depending on the dataset) may not be easily interpretable. One could draw a comparison to Principal Component Analysis and the result components as features.
One benefit of using Deep Belief Nets to pre-train your model and encode features is that the data must not be labelled. This leads to the final point, which is that your 10 features must not necessarily have names. You can just call them e.g. feature1, feature2, ..., feature10.
If you are having problems with the actual code and the way to push the 10 features further into the classifier, I would suggest you provide the code that you have so far and add more detail regarding your exact problem.
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