Active semi-supervised classification based on multiple clustering hierarchies
Full text
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| Author(s): |
Antônio José de Lima Batista
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2016-08-08 |
| Examining board members: |
Ricardo José Gabrielli Barreto Campello;
Rodrigo Coelho Barros;
Estevam Rafael Hruschka Júnior;
Rodrigo Fernandes de Mello
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| Advisor: | Ricardo José Gabrielli Barreto Campello |
| Abstract | |
Active semi-supervised learning can play an important role in classification scenarios in which labeled data are laborious and/or expensive to obtain, while unlabeled data are numerous and can be easily acquired. There are many active algorithms in the literature and this work focuses on an active semi-supervised algorithm that can be driven by clustering hierarchy, the well-known Hierarchical Sampling (HS) algorithm. This work takes as a starting point the original Hierarchical Sampling algorithm and perform changes in different aspects of the original algorithm in order to tackle its main drawbacks, including its sensitivity to the choice of a single particular hierarchy. Experimental results over many real datasets show that the proposed algorithm performs superior or competitive when compared to a number of state-of-the-art algorithms for active semi-supervised classification. (AU) | |
| FAPESP's process: | 14/01352-0 - Active, semi-supervised learning for classification and clustering |
| Grantee: | Antônio José de Lima Batista |
| Support Opportunities: | Scholarships in Brazil - Master |