Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …

In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria
decision making (MCDM) process. This method is applied to a multi-label data and we have …

Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …

Annotating multiple organs in medical images is both costly and time-consuming; therefore,
existing multi-organ datasets with labels are often low in sample size and mostly partially …

Multi-label learning generalizes traditional learning by allowing an instance to belong to
multiple labels simultaneously. This causes multi-label data to be characterized by its large …

Multi-label data with high dimensionality, widely existed in the real world, bring many
challenges to the applications of machine learning, pattern recognition and other fields …

In the multi-label classification task, an instance is simultaneously associated with multiple
semantic labels. Due to the high complexity of the semantic space in practical applications …