Hi, I've been dabbling with Mahout off and on for a few months preparing for a classification project. It's now time to stop experimenting and do something for real. I've picked up a lot of things from following this list, but would like some advice regarding a few things before proceeding. I'll start with a very brief description of the project and then follow up with some questions.
We need to classify potentially millions of documents into about 100 or so categories. Most documents will probably only belong to 1 category, but some will belong to several. It's also possible for some documents to not belong to any of the chosen categories. As noted, we need to handle the case where a document belongs to multiple categories. My understanding is the classification algorithms are primarily geared to classifying an item into one category and we would need run multiple classifiers in parallel to match multiple categories. Is that correct? I found something in the subversion logs referencing "multilabel" support that sounded interesting, but it was removed a few weeks ago. Is that of any relevance? Also as noted, we need to handle the case where a document belongs to no categories. Do any of the classification algorithms support the concept of an implicit "other" or "none" category or do we need to add an explicit one? If the latter, how many training samples do we need to use compared to the number of samples for the target categories? Finally, I recall seeing on this list that some of the classification algorithms break down if more than 20 to 30 categories are used and that multiple classifiers should be used hierarchically when more categories are needed. Is that still correct? If so, is there any preferred way to organize the cascaded classifiers? I'm currently analyzing the documents we will use for training to see which categories often, seldom or never occur together. David -- David Engel [email protected]
