"Cluster model for classification?"

harri678harri678 Member Posts: 34 Contributor II
edited June 2019 in Help

after reading some papers about Support Vector Clustering I tought this method is perfect for my type of work, but now it doesn't look like it.

I want to classify data as "normal" or "anormal", but only have "normal" labeled data for training. The thing i tried to do was to generate an SVC-model which contains one single cluster of normal data. After calculation i wanted to apply the generated clustermodel on the testing data which also contains anormal sets. I thought the model will calculate the affiliation to the normal-cluster, but it seems I am wrong. Is there any way to do this in RapidMiner beneath one class svm's?



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    landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi Harald,
    at least there's no such ready to go method I'm currently aware of. You might use simply a distance threshold to the nearest neighbor, but this isn't exactly the same. I could think of extending the SV Clustering in this way or implementing something like a normal distribution kernel based method, but if this would work depends on your data. Seems to be a rather complex setting :)

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    harri678harri678 Member Posts: 34 Contributor II
    Hi Sebastian,

    thanks for the response!
    It seems i have to look for other solutions of my problem.
    I tried to use the libsvm's one-class in rapidminer-5.0-rc and there I have other problems (not shure if this is a bug).
    When i use "Generate Data" to produce ExampleSets, the one-class libsvm fails with "numeric label not supported.". The same error is produced with other types of label. If I filter the label, it fails with "... does not have a label attribute.". In fact I wasn't able to get libsvm running for once. Do I have to do some XML tuning to prevent the label-madness or is there another way to train libsvm via rapidminer?

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