Binary relevance for multi-label learning: an overview
Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …
instance while being associated with multiple class labels simultaneously. Binary relevance …
A review on multi-label learning algorithms
Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …
instance while associated with a set of labels simultaneously. During the past decade …
Deep hierarchical semantic segmentation
Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …
complex scenes into simpler parts and abstract the visual world in multiple levels. However …
Logic-induced diagnostic reasoning for semi-supervised semantic segmentation
Recent advances in semi-supervised semantic segmentation have been heavily reliant on
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …
Predicting multicellular function through multi-layer tissue networks
M Zitnik, J Leskovec - Bioinformatics, 2017 - academic.oup.com
Motivation Understanding functions of proteins in specific human tissues is essential for
insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular …
insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular …
Learning spatial regularization with image-level supervisions for multi-label image classification
Multi-label image classification is a fundamental but challenging task in computer vision.
Great progress has been achieved by exploiting semantic relations between labels in recent …
Great progress has been achieved by exploiting semantic relations between labels in recent …
A tutorial on multilabel learning
E Gibaja, S Ventura - ACM Computing Surveys (CSUR), 2015 - dl.acm.org
Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …
increasing number of fields where it can be applied and also to the emerging number of …
Embeddings from deep learning transfer GO annotations beyond homology
Knowing protein function is crucial to advance molecular and medical biology, yet
experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5 …
experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5 …
A survey of hierarchical classification across different application domains
CN Silla, AA Freitas - Data mining and knowledge discovery, 2011 - Springer
In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …
is scattered across very different application domains and for that reason research in one …
Mining multi-label data
A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …
where training examples are associated with a single label λ from a set of disjoint labels L …