Data classification is a task with high applicability in a lot of areas. Most methods for treating classification problems found in the literature dealing with single-label or traditional problems. In recent years has been ...

Multi-label classification is a machine learning task where instances can be classified into two or more labels simultaneously. In this task, there exist correlations between the instances belonging to same or similar sets ...

This work presents a new method for multi-label classification based on Particle Swarm Optimization, called Multi Label K-Nearest Michigan Particle Swarm Optimization ML-KMPSO) and evaluates it experimentally using two ...

Multi-label classification a machine learning task whose objective is to generate models capable of learning relationships between descriptive characteristics of objects and the sets of classes to which such objects belong. ...

In hierarchical multi-label classification problems, each instance may be associated with one or more labels simultaneously belonging to a hierarchical level subclass or superclass. This classification is performed through ...

Protein subcellular localization is a really important classification task, because the location of proteins inside a cell is directly related to these protein’s functions. As there are a lot of proteins that reside at the ...

Classification is the task of assigning data instances to classes. In Hierarchical Multi- label Classification, instances may belong to two or more classes (labels) simultaneously, where the classes are hierarchically ...

The techniques of Machine Learning are applied in classification tasks to acquire knowledge through a set of data or information. Some learning methods proposed in literature are methods based on semissupervised learning; ...