An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
Classification problems involving multiple classes can be addressed in different ways. One
of the most popular techniques consists in dividing the original data set into two-class …
of the most popular techniques consists in dividing the original data set into two-class …
Towards enabling binary decomposition for partial multi-label learning
Partial multi-label learning (PML) is an emerging weakly supervised learning framework,
where each training example is associated with multiple candidate labels which are only …
where each training example is associated with multiple candidate labels which are only …
On the decoding process in ternary error-correcting output codes
A common way to model multiclass classification problems is to design a set of binary
classifiers and to combine them. Error-correcting output codes (ECOC) represent a …
classifiers and to combine them. Error-correcting output codes (ECOC) represent a …
Disambiguation-free partial label learning
In partial label learning, each training example is associated with a set of candidate labels
among which only one is the ground-truth label. The common strategy to induce predictive …
among which only one is the ground-truth label. The common strategy to induce predictive …
Joint binary classifier learning for ECOC-based multi-class classification
Error-correcting output coding (ECOC) is one of the most widely used strategies for dealing
with multi-class problems by decomposing the original multi-class problem into a series of …
with multi-class problems by decomposing the original multi-class problem into a series of …
Learning label-specific features for decomposition-based multi-class classification
Multi-class classification can be solved by decomposing it into a set of binary classification
problems according to some encoding rules, eg, one-vs-one, one-vs-rest, error-correcting …
problems according to some encoding rules, eg, one-vs-one, one-vs-rest, error-correcting …
Biometric information recognition using artificial intelligence algorithms: A performance comparison
Addressing crime detection, cyber security and multi-modal gaze estimation in biometric
information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms …
information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms …
Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers
Abstract The One-vs-One strategy is one of the most commonly used decomposition
technique to overcome multi-class classification problems; this way, multi-class problems …
technique to overcome multi-class classification problems; this way, multi-class problems …
A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks
Decision tree algorithms have been proved to be a powerful and popular approach in
classification tasks. However, they do not have reasonable classification performance in …
classification tasks. However, they do not have reasonable classification performance in …
Subclass problem-dependent design for error-correcting output codes
A common way to model multiclass classification problems is by means of Error-Correcting
Output Codes (ECOCs). Given a multiclass problem, the ECOC technique designs a code …
Output Codes (ECOCs). Given a multiclass problem, the ECOC technique designs a code …