What's the right structure Deep Learning structure for Multilabel classification

Question

I have the following data

         feat_1    feat_2 ... feat_n   label
gene_1   100.33     10.2  ... 90.23    great
gene_2   13.32      87.9  ... 77.18    soso
....
gene_m   213.32     63.2  ... 12.23    quite_good

The size of M is large ~30K rows, and N is much smaller ~10 columns. My question is what is the appropriate Deep Learning structure to learn and test the data like above.

At the end of the day, the user will give a vector of genes with expression.

gene_1   989.00
gene_2   77.10
...
gene_N   100.10

And the system will label which label does each gene apply e.g. great or soso, etc...

By structure I mean one of these:

• Convolutional Neural Network (CNN)
• Autoencoder
• Deep Belief Network (DBN)
• Restricted Boltzman Machine

Answer 1

I suggest that a feedforward Neural Network with 1 or more hidden layers should be appropriate for a binary or multiclass problem.

I should also point out that with a relatively smaller number of features (10) compared to M, other classifiers such as Logistic regression / Random Forests should give acceptable performance and would train faster as well.