Hi,
I'm currently using Random Forests to classify acoustic data to corresponding bat species, however I was wondering if there is a way to also incorporate unsupervised cluster analysis into my classifier (such as K-Means or Hierarchical modelling)?
Whilst I have training data for ~40% of my target species, there is likely to be a large proportion of my field dataset which corresponds to species that haven't yet been described in the supervised model and therefore I would like for the classifier to be able to assign new clusters/call types to new classes (that hopefully can be matched to a species later).
I have been looking at consensus maximisation algorithms but I'm not sure if this would be the best method.
Any help is greatly appreciated!
(I'm not a computer scientist so please forgive me if this is a very straightforward question)
Hi,
you can use data clustering alorithms (Kmean , EM , mean shift ....) and also one class SVM
Hello
Semi supervised method is a combination of supervised and unsupervised ML methods. When you need more data to increase the accuracy of your model, and when you do not have the ability to collect more labeled data, you may use the semi supervised methods to increase the size of your training data-set and have more accurate model
Thank you for your answers!
Unfortunately there are several classes that are absent from my training dataset (rather than just not having enough training data for a given class) therefore reinforcement learning wouldn't be appropriate. It has not been possible to gather data for all given species in the region. Therefore I'm looking for a way for the classifier to recognise that there may be additional classes in the data through some form of clustering mechanism, despite the fact it isn't part of the supervised component.
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