The practical techniques for such problems are pretty diverse. One method is to simply define multiple binary classifiers. If you can stratify your labels, then you can have some labels depend on others. Another option is to find commonly occurring sets of labels and build classifiers for those sets directly.
Do you have an application mind? Do you have data available? On Tue, Nov 8, 2011 at 6:37 AM, David Rahman <[email protected]>wrote: > Yes, I was asking for an example where multiple labels might be aplied to a > single example. > > Thanks and regards, > David > > 2011/11/8 Ted Dunning <[email protected]> > > > What exactly do you mean by multi-label classification? > > > > The 20 newsgroup example has many possible label values. > > > > Are you asking for an example where multiple labels might be applied to a > > single example? If so, no, we don't have a nice example of that. > > > > On Tue, Nov 8, 2011 at 5:36 AM, David Rahman <[email protected] > > >wrote: > > > > > Hi, > > > > > > I have a general question about multi-label classification. Binary- or > > > single-label classification is working, as shown in several examples > > > (Wikipedia and 20Newsgroup, Mahout In Action book...). > > > > > > Are there some working examples on multi-label calssification for > trying > > > out? > > > Or is there some data available on how mahout performs on mult-label > > > classification problems? > > > > > > Thanks and regards, > > > David > > > > > >
