The gist is me wanting to separate system faults from sensor faults given some dataset from a wireless sensor network using a machine learning algorithm.
For instance, if I have some temperature sensors in a given area and their corresponding readings from every sort of time interval, I would like to know whether an abnormal value is due to an actual fault, or due to a faulty sensor. Of course, it would be a given that the training set would have such entries tagged with either sensor fault or system fault.
I have thought of just using something like linear regression but I would like it to work even if the system could not be modeled like that. Decision tree seemed to me like a more appropriate algorithm for this.
Lastly, there is also some consideration for the time it takes for training and classification as I wish to see if it can be used for systems which respond really quickly to such anomalies.
Sorry if it's a bit wordy but I wasn't sure how much information I should put since this is my first time posting (I'm not even sure if this is the right stack exchange to post this). Anyway, thanks in advance for the answers!