Long-term Cognitive Network-based architecture for multi-label classification

Multi-label classification with Bayesian network-based chain classifiers

Article history: Available online 20 November 2013

Multi - label classification with Bayesian network - based chain classifiers q

In multi-label classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of labels (label power-set methods) or by building independent classifiers for each class (binary relevance methods). The first approach suffers from high computationally complexity, while t...

Boosting-based Multi-label Classification

Multi-label classification is a machine learning task that assumes that a data instance may be assigned with multiple number of class labels at the same time. Modelling of this problem has become an important research topic recently. This paper revokes AdaBoostSeq multi-label classification algorithm and examines it in order to check its robustness properties. It can be stated that AdaBoostSeq ...

Efficient Two Stage Voting Architecture for Pairwise Multi-label Classification

A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of la...

A multi-label classification based approach for sentiment classification