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Active learning algorithms for multi-label data

Grant number: 11/21723-5
Support type:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): June 01, 2012
Effective date (End): October 31, 2012
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Maria Carolina Monard
Grantee:Everton Alvares Cherman
Supervisor abroad: Grigorios D. Tsoumakas
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: Aristotle University of Thessaloniki (AUTh), Greece  
Associated to the scholarship:10/15992-0 - Exploring label dependency in multilabel learning, BP.DD


Due to the increasing number of new applications where examples are annotated with more than one label, multi-label classification has attracted the attention of the academic community. Multi-label classification is being used in an increasing number of applications such as semantic annotation of video and image, functional genomic and music categorization into emotions, to name just a few. In multi-label application, as well as in single-label, unlabeled data vastly outnumber labeled data because of the expensive labeling cost in classification applications. However, it is possible to minimize labeling costs by allowing the learner to query for the most informative data points. This is the goal of active learning, which has been often used in single-label data, and only recently its use has been extended to multi-label data. The overall goal of this research project is to explore active learning on multi-label data. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CHERMAN, EVERTON ALVARES; PAPANIKOLAOU, YANNIS; TSOUMAKAS, GRIGORIOS; MONARD, MARIA CAROLINA. Multi-label active learning: key issues and a novel query strategy. EVOLVING SYSTEMS, v. 10, n. 1, SI, p. 63-78, MAR 2019. Web of Science Citations: 0.

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