I am working on flood inundation mapping using remote sensing data. I am using a single band, a simple threshold value can be used to separate land and water. I am interested in knowing, can we use SVM and Random forest for classification on one-dimensional data?
Yes, this is possible.
Consider a binary classification problem with real-valued (1d) inputs and class labels '0' and '1'. A linear classifier will find a decision boundary that separates the input space into two halves. In the 1d case, this is simply a threshold. Points will be assigned label '0' on one side of the boundary and label '1' on the other side. For example:
0 0 0 0 0 | 1 1 1 1
If the data fit this pattern, then a linear classifier is a good option. A non-linear classifier (e.g. random forest or kernelized SVM) is more appropriate when data from each class are distributed in multiple, interleaved regions, and can't be separated by a single threshold. For example:
0 0 0 0 | 1 11 11 1 | 0 0 0 0 0 0