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Multi-label classification

Known as: Algorithm adaptation methods, Multi-label, RAKEL 
In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification… 
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Related topics

Related topics

24 relations
AdaBoostBinary classificationBoosting (machine learning)Categorization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Multi-label text classification using optimised feature sets
Multi-label text classification is the process of assigning multi-labels to an instance. A significant aspect of the text… 
2016
2016
Benchmarking Multi-label Classification Algorithms
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 20-21 September 2016 
2014
2014
Robust Semi-Automatic Depth Map Generation in Unconstrained Images and Video Sequences for 2D to Stereoscopic 3D Conversion
We describe a system for robustly estimating synthetic depth maps in unconstrained images and videos, for semi-automatic… 
2014
2014
LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion
Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the… 
Highly Cited
2013
Highly Cited
2013
GURLS: a least squares library for supervised learning
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted… 
2013
2013
Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs
The imbalanced sentiment distribution of microblogs induces bad performance of binary classifiers on the minority class. To… 
2011
2011
Ontology-based multi-label classification of economic articles
The paper presents an approach to the task of automatic document categorization in the field of economics. Since the documents… 
Highly Cited
2010
Highly Cited
2010
An integration of WordNet and fuzzy association rule mining for multi-label document clustering
2010
2010
Multi-Task Sparse Discriminant Analysis (MtSDA) with Overlapping Categories
Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of… 
2008
2008
Extracting Shared Subspaces for Multi-label Classification
Multi-label problems arise in various domains such as multi-topic web page categorization, protein function prediction, and… 
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