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Aggregating Independent and Dependent Models to Learn Multi-label Classifiers
Published on Nov 30, 20113548 Views
The aim of multi-label classification is to automatically obtain models able to tag objects with the labels that better describe them. Despite it could seem like any other classification task, it is w
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Chapter list
Aggregating Independent and Dependent Models to Learn Multi-label Classifiers00:00
Outline - 100:10
Outline - 200:24
The Goal00:26
Outline - 300:51
Multi-label vs. mono-label classification00:53
Many applications01:21
Formal statement01:28
Evaluating multi-label classification01:55
Outline - 402:29
Binary relevance02:32
Stacking approaches02:49
Classifier chains03:15
Other approaches - 103:50
Other approaches - 204:03
Other approaches - 304:21
Outline - 504:36
Aggregating Independent and Dependent classifiers (AID)04:39
Comparing AID with other methods - 105:52
Comparing AID with other methods - 206:14
About the complexity06:50
Outline - 607:12
Settings07:13
AID vs. Stacking07:45
AID vs. other methods08:47
Outline - 710:24
Conclusions11:28