Partial multi-label learning refers to the problem that each instance is associated with a
candidate label set involving both relevant and noisy labels. Existing solutions mainly focus …

We propose a sentiment classification method for large scale microblog text based on the
attention mechanism and the bidirectional long short-term memory network (SC-ABiLSTM) …

The importance of data quality and quantity cannot be overstated in automatic data analysis
systems. An important factor to take into account is the capability to assign a data item to …

Existing online multi-label classification works cannot well handle the online label
thresholding problem and lack regret analysis for their online algorithms. This paper …

In Multi-Label Classification (MLC), each data sample is characterized by multiple labels. In
ML, there is no restriction on the number of classes a data sample could belong to. Various …

Label distribution learning (LDL) explicitly models label ambiguity based on the degree to
which each label describes an instance. Existing LDL algorithms typically consider all …

Partial multi-label (PML) learning refers to the modeling of prediction patterns from data
annotated with partially correct labels. Label embedding that finds a compact representation …

Regression and classification are the two main learning tasks in supervised learning, and
both of them can be solved by learning a hyperplane from training samples. However, the …

One of the crucial pre-processing stages in data mining and machine learning is feature
selection, which is used to choose a subset of representative characteristics and decrease …

This paper introduces a novel online multi-label classification approach that focuses on
optimizing the distribution of multi-label classification margins. The objective is to find a new …