I am trying to classify a topographic cross section (profile) using a machine learning method. The classification consists of 2 main classes (scarp, no scarp) and 3 sub-classes (cls1, cls2, and cls3) that correspond to easternward and westernward inclined scarps, or flat areas, respectively.
These data are normalized, and I have 16 variables that may distinguish the classes I need. The expression of these data vary from profile to profile, and instead of using a rule-based system, I would like to get the classes for each topographic data point probabilistically.
However, the machine learning method to use is uncertain (neural network (NN)? Regression Tree? or SVM?). For Regression Tree or SVM, I have re-worked the representation of the data to get the dataset 2 in Fig.2.
For the 1st representations of data (data set 1), I tried a non-linear classification approach using a NN system (built with TensorFlow). However, after training and testing, I get 80 to 85% accuracy for the first, and only 50% accuracy for the second... Note that I have 1700 profiles in total (multiplied by around 500 points), and I used only 70 profiles as training data.
My question is:
Should I consider using the 2nd set of data for classification with Regression Tree or SVM, or would you advise me to still consider the NN approach but improve it (through, e.g. more data, better representation...).