Multi-Label Learning
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 December 2019) | Viewed by 457
Special Issue Editors
Interests: machine learning; data mining, big data; genetic programming and evolutionary computation; computational intelligence
Interests: artificial intelligence; machine learning; data mining; metaheuristics; algorithms and data structures
Special Issue Information
Dear Colleagues,
In the last two decades, research on multi-label learning has received increasing attention, mainly because many real-world problems can be modeled under this learning paradigm. Such an interest can be appreciated in the continuous apparition of methods to different tasks, ranging from developing approaches for feature and instance selection, the correction of the imbalance between labels, noise detection, and the reduction of feature and label spaces, to the adaptation of algorithms to directly handling the multi-label data, the combination of active and semi-supervised approaches for constructing better classifiers, the development of methods for extreme multi-label classification, deep models, and so on. Multi-label learning is an exciting area that involves a set of challenges and difficulties that usually do not appear in other learning paradigms, such as the automatic modeling of inter-label correlations, treating the imbalance between labels, and the unfeasibility of using certain approaches to problems with a large number of labels. Despite the considerable number of existing works, several of the issues mentioned above remain open. Furthermore, it is necessary to continue the development of more efficient classifiers. Therefore, the aim of this Special Issue is to enhance the state-of-the-art in the multi-label learning area significantly. We exhort authors across the world to submit their original and unpublished works. We have an especial interest in works focusing on the topics listed below, but works attending other approaches will also be well received.
Prof. Dr. Sebastián Ventura
Dr. Oscar Reyes
Guest Editors
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Keywords
- multi-label feature selection
- multi-label instance selection
- label space reduction
- noise detection
- active multi-label learning
- semi-supervised multi-label learning
- extreme multi-label classification
- multi-label deep learning
- ensemble-based multi-label classification
- multi-label classification with missing labels
- improving effectiveness via handling label correlations
- imbalance multi-label learning
- streaming multi-label classification
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