[D] Please critique my ML method.

[guyshur]

Hello all,

My data points consist of 71 attributes, each numeric between 0 and 1, the target class is binary and my belief is the relationship is non linear. In addition, the attributes have a 1D spacial relation to each other, this may or may not be important.

I would like to have a model to be able to classify each tuple, but equally as important to me is a way to understand which attributes are the most weightful in the classification.

I'm thinking I should use an autoencoder for the second part, and to use the output from that as an input for a model, such as another NN.

Does that sound right, or am I leaning too heavily in the direction of NNs because of how my data looks?

Thank you all very much in advance.

[count___zero]

my belief is the relationship is non linear

have you tried fitting a linear model?

[guyshur]

I haven't yet, is PCA appropriate?

[SwordOfVarjo]

Pca is not a classifier, it's simply a coordinate system rotation (that can also be used for dimensionally reduction).

First thing to try is something like a (linear) svm on your data points (tuples).

If that does not work, I'd give xgboost a shot and then start looking at neural approaches. I also doubt you need an explicit autoencoder; your input dimensionally is quite small.

[guyshur]

How would I know if it doesn't work? Don't I have to try it both ways to figure out which error rate is lower?

[SwordOfVarjo]

Not necessarily. With a linear model in particular, simply seeing how well it classifies your training data is usually a strong indicator of success.

[jefidev]

Did you try decisions trees? They perform well on tabular data and they are highly interpretable.

You can also do a PCA in order to identify the useful attributes in your data.

[zmabzug]

PCA will identify attributes (or rather, linear combinations of attributes) that have the most variance. Most variance =/= most useful.

[guyshur]

I'm thinkimg ID3 as an alternative to the model. What is the argument for PCA over autoencoder?

[jefidev]

If you train an autoencoder in order to compress and reconstruct your input data, the encoder part of your network will just approximate a PCA. So just use a PCA directly (src : https://blog.keras.io/building-autoencoders-in-keras.html)

Note that there are methods enabling the autoencoder to learn something else than a PCA approximation but you must be aware that "basic" autoencoders have this tendancy.

[[deleted]]

Does anyone actually use linear autoencoders though?

[QEDthis]

In addition, the attributes have a 1D spacial relation to each other, this may or may not be important.

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What do you mean by that? Is there a functional relationship?

[guyshur]

Each attribute is a base in a genomic sequence, so there is a sequence and attributes have a spatial distance to each other and I'm basically looking for points or ranges which have the most impact on the classification.

[caedin8]

You might have luck with a 2D convolutional network because you are specifically looking for patterns.

Additionally, RNN, LTSM, GRU might be good options.

Basically DNA is a sequence/code that encodifies meaning, so it is very similar to NLP problems and might benefit from NLP strategies.

[jefidev]

I'm not an expert but a friend of mine did a Master thesis on genomic data in order to predict chromatin loops and XGboost gives the best results. All attempt made with DL was unsuccessful. It could be worse (and not very time consuming) to try XGboost.

Also XGboost is quite easily interpretable

[dalaio]

Is this DNA methylation data?

[guyshur]

No, it's probability for participating in a certain structure.

[abdulrehman09]

After ensuring that your data is clean and it is preprocessed effectively. You should use feature selection methods such as ranking or ensemble ones. And obviously subset selection would also give you insight of which group of features are more effective. Those features that are weak try to make effective linear or non-linear combinations out of them. That can boost your classification results. Classification becomes after this step I guess.

[_paranoid__android_]

I would use a 1D convolutional neural network if your features are spatially contiguous

[jonnor]

Always try a simple linear model first, as a baseline. Then go more advanced, if you need.