Multi-label stream classification aims to address the challenge of dynamically assigning
multiple labels to sequentially-arrived instances. In real situations, only partial labels of …

Multi‐label feature selection eliminates irrelevant and redundant features, and then
improves the performance of multi‐label classification models. Most multi‐label feature …

Multi-label classification originated from text classification and has became one of the most
widely studied machine learning frameworks. After nearly twenty years of development …

Multi-label classification with missing labels handles the problem that the label set contains
unobserved missing labels due to the expensive human annotations. However, these works …

Business intelligence process analyzed data and found certain insights so that managers,
executives, or higher authorities of the enterprise can take appropriate decision. Business …

The label-specific features learning is a kind of framework for extracting the specific features
of each label for classification. At present, the label-specific features algorithm is generally …

We consider the positive-unlabelled multi-label scenario in which multiple target variables
are not observed directly. Instead, we observe surrogate variables indicating whether or not …

现有的多标签学习算法往往只侧重于实例空间到标签空间的正向投影, 正向投影时由于特征维数
降低所产生的实例空间信息丢失的问题往往被忽略. 针对以上问题, 提出一种基于双向映射学习 …

In Multi-label (ML) classification, each instance is associated with more than one class label.
Incorporating the label correlations into the model is one of the increasingly studied areas in …

Label distribution learning (LDL) is a framework to solve label ambiguity. To improve the
performance of LDL, some existing algorithms exploit global and local label correlations. In …