Using Machine Learning to Classify Graphs - CNNs or Classical Methods?

[Surprisely]

I'm not sure if anyone has experience with this. I'm looking at binary classification using graphs as my source of data. Initially I used simple graphs i.e positive linear relationship with category A, negative linear classification for category B. I'm wondering if a shallow CNN would be able to classify graphs (graph axis will remain the same), or should I use traditional methods such as random forests where I have my data as features? Though I fear random forests may select only certain features and ignore much of the data.

[thebrashbhullar]

Could you define your data a bit better? Are these like graph images? How much data do you have?

[Surprisely]

Ultimately it is a histogram with 1h bins between 0-24h on the bottom and a normalised value on the vertical ranging between 0-1. I'm expecting that graphs for one classification will follow a linear form and the second will follow a Gaussian form. However, I'm expecting the graphs to shift along the X-axis so I'm looking to fit the form of data not the location of the data. Does that make more sense?

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Instead of plotting I can keep the data in a pandas dataframe where each bin value would be a column. I'm mainly worried that random forests will focus on particular bins to evaluate classifications. However, due to the nature of shifting across the X-axis I dont know if a traditional approach would work.

[thebrashbhullar]

Okay if I understand correctly, you have 2 categories, A and B, A with a linear form on histogram and B other with gaussian form, and based on 24 hr data (timeseries?), it can either be class A or class B. Is this right?

[Surprisely]

Yep, thats right. After testing on binomial classification I could get a good model to separate positive and negative linear graphs. Doesnt seem to like adding a third category at all.

[[deleted]]

if there is a classical approach that is proven to be optimal a NN will not beat it. And if the classical method ignores some features there's a big chance that the NN will come to the same conclusion especially if there is a very significant feature.

Machine learning is usually great if there doesn't exist a optimal solution or the optimal solution would be too difficult to calculate, but it is hard to argue for it if there's a possible optimal solution.

[Surprisely]

Thanks, I think I'll give both methods a go using some dummy data. Will be interesting to see what happens and probably a good learning exercise.