Background: The idea of smart healthcare has gradually gained attention as a result of the
information technology industry's rapid development. Smart healthcare uses next-generation …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Multilabel data contains rich label semantic information, and its data structure conforms to
the cognitive laws of the actual world. However, these data usually involve many irrelevant …

Feature selection is one of the most efficient procedures for reducing the dimensionality of
high-dimensional data by choosing a practical subset of features. Since labeled samples are …

In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …

In recent years, multi-label learning becomes a trending topic in machine learning and data
mining. This type of learning deals with data that each instance is associated with more than …

For the first time, the ensemble feature selection is modeled as a Multi-Criteria Decision-
Making (MCDM) process in this paper. For this purpose, we used the VIKOR method as a …

The performance of multilabel learning depends heavily on the quality of the input features.
A mass of irrelevant and redundant features may seriously affect the performance of …

Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …