Embedding Methods for Predicting Multilabel Interactions between piRNAs and Transp...
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Author(s): |
Everton Alvares Cherman
Total Authors: 1
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Document type: | Doctoral Thesis |
Press: | São Carlos. |
Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
Defense date: | 2014-01-10 |
Examining board members: |
Maria Carolina Monard;
José Augusto Baranauskas;
Gustavo Enrique de Almeida Prado Alves Batista;
Alexandre Plastino de Carvalho;
Altigran Soares da Silva
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Advisor: | Maria Carolina Monard |
Abstract | |
Traditional supervised learning methods, called single-label learning, consider that each example from a labeled dataset is associated with only one label. However, an increasing number of applications deals with examples that are associated with multiple labels. These applications require multi-label learning methods. This learning scenario introduces new challenges and demands approaches that are different from those traditionally used in single-label learning. The cost of labeling examples, a problem in single-label learning, is even higher in the multi-label context. Developing methods to reduce this cost represents a research challenge in this area. Moreover, new learning methods should also be developed to, among other things, consider the label dependency: a new characteristic present in multi-label learning problems. Furthermore, there is a consensus in the community that multi-label learning methods are able to improve their predictive performance when label dependency is considered. The main aims of this work are related to these challenges: reducing the cost of the labeling process; and developing multi-label learning methods to explore label dependency. In the first case, as well as other contributions, a new multi-label active learning method, called score dev, is proposed to reduce the multi-labeling processing costs. Experimental results show that score dev outperforms other methods in many domains. In the second case, a method to identify label dependency, called UBC, is proposed, as well as BR+, a method to explore this characteristic. Results show that the BR+ method outperforms other state-of-the-art methods (AU) | |
FAPESP's process: | 10/15992-0 - Exploring label dependency in multilabel learning |
Grantee: | Everton Alvares Cherman |
Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |