0.25
0.5
0.75
1.25
1.5
1.75
2
Streaming Multi-label Classification
Published on Nov 11, 20116111 Views
This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best pr
Related categories
Chapter list
Streaming Multi-label Classi cation00:00
Introduction: Streaming Multi-label Classi cation - 100:10
Introduction: Streaming Multi-label Classi cation - 201:38
Applications of Multi-label Learning - 102:46
Applications of Multi-label Learning - 204:53
Methods for Multi-label Classi cation - 105:34
Methods for Multi-label Classi cation - 206:20
Problem Transformation Methods - 106:53
Problem Transformation Methods - 208:35
Algorithm Adaptation11:02
Multi-label Learning in Data Streams - 111:43
Multi-label Learning in Data Streams - 212:40
Multi-label Learning in Data Streams - 314:31
Dealing with Concept Drift - 114:43
Dealing with Concept Drift - 215:30
WEKA16:41
MOA17:08
MEKA17:53
A Multi-label Learning Framework for Data Streams19:23
Evaluation - 119:49
Generating Synthetic Data - 121:26
Generating Synthetic Data - 222:03
GUI: Con guring a multi-label classi er23:21
GUI: Setting a multi-label stream generator23:50
Methods24:16
Data sources25:37
Evaluation - 227:02
Summary and Future Work29:29
References30:25