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Classifier chains

Classifier chains is a machine learning method for problem transformation in multi-label classification. It combines the computational efficiency of… 
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Related topics

Related topics

4 relations
Ensemble learningMachine learningMulti-label classificationSupport vector machine

Papers overview

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2018
2018
Dynamic Classifier Chain with Random Decision Trees
Classifiers chains (CC) is an effective approach in order to exploit label dependencies in multi-label data. However, it has the… 
2017
2017
Ordering Classifier Chains using filter model feature selection techniques
Context: Multi-label classification concerns classification with multi-dimensional output. The Classifier Chain breaks the multi… 
2017
2017
Naive Bayes Classifier for Dynamic Chaining Approach in Multi-label Learning
In this paper, we addressed an issue of building dynamic classifier chain ensembles for multi-label classification. We built a… 
2015
2015
Polytree-Augmented Classifier Chains for Multi-Label Classification
Multi-label classification is a challenging and appealing supervised learning problem where a subset of labels, rather than a… 
2015
2015
Context-aware MIML instance annotation: exploiting label correlations with classifier chains
In multi-instance multi-label (MIML) instance annotation, the goal is to learn an instance classifier while training on a MIML… 
2013
2013
Multi-Label Classification Using Dependent and Independent Dual Space Reduction
While multi-label classification can be widely applied for problems where multiple classes can be assigned to an object, its… 
2011
2011
A Two-Step Method to Learn Multidimensional Bayesian Network Classifiers Based on Mutual Information Measures
Bayesian Network Classifiers are popular approaches for classification problems where instances have to be assigned to one of… 
2010
2010
Sorted label classifier chains for learning images with multi-label
In the real world, images always have several visual objects instead of only one, which makes it difficult for conventional… 
2009
2009
Classification tree Sources
The separation of source coding into two stages, modeling and encoding, is a highly successful approach. We propose meta-modeling… 
2009
2009
Simulated Iterative Classification A New Learning Procedure for Graph Labeling
Collective classification refers to the classification of interlinked and relational objects described as nodes in a graph. The… 
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