Doubts about the algorithms in MEKA about meka

[jmread]

Hi,

• CM does not take into account posterior probabilities (and thus may be used when none are available). This method is not specific to Meka, More details can be found in the literature.

• In MBR the label predictions of a BR method become the features for a new BR.

• In Subset Mapping, it refers to the Hamming distance between a prediction on a test instance and label combinations (i.e., label subsets) from the training set, it is 'mapped' to the closest one.

Cheers,

Jesse

[alexgcsa]

Thanks, Jesse.

I was also checking the code of Four-class pairWise classification algorithm (FW) and I didn't understand so much one of the methods (distributionForInstance) :

https://github.com/Waikato/meka/blob/master/src/main/java/meka/classifiers/multilabel/FW.java

I was wondering if the result (output) of this method should be the probabilities of the labels. It seems that is just the countings for each possible class and outputting the countings. I got a little confused at this point (I changed the name of issue because of it).

Thank you so much again.

Best regards,

Alex de Sa

[jmread]

Your understanding of the FW classifier is correct. Probably it would be nicer to divide the counts by a sum to obtain a kind of approximation of the probability (between 0 and 1) for each label. But it is not a requirement, since an appropriate threshold will take care of that.

Regards,

Jesse

[alexgcsa]

Alright. I understood what you said. I will possibly do that in the code then.

Best regards,

Alex de Sa