Can someone suggest an algorithm for an outlier detection system? My requirements are:
- it supposed to be a one class classifier, where on training phase, it only fed 'normal' data
- however the normal class can have multiple sub-class. (we can see it as multi-class classification with an 'other' class, where this 'other' class is never learned)
- all features are categorical
- features have sequence relation, so I need it analysed in sequence.
- I need a lighter algorithm for real time application
- optionally - I need an on-line learning capability
I have tried HMM. However it still very heavy to my liking. Furthermore it required me to create different model for each sub-normal-class.
Next I tried one-class-svm (with one hot encoder). It was light enough. However results was incorrect. I'm guessing it was because this approach is not appropriate for sequence analysis?
Now I am in the middle of Neural Network trial. I read that Autoencoder is an approach for one-class-classification. My results so far is not satisfying in term of learning time and detection results.
Can someone suggest a better (machine learning algorithm) approach?