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| Author(s): |
Victor Hugo Barella
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: | 2015-07-24 |
| Examining board members: |
André Carlos Ponce de Leon Ferreira de Carvalho;
Fernando Vieira Paulovich;
Ronaldo Cristiano Prati
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| Advisor: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Abstract | |
Recent advances in science and technology have made possible the data growth in quantity and availability. Along with this explosion of generated information, there is a need to analyze data to discover new and useful knowledge. Thus, areas for extracting knowledge and useful information in large datasets have become great opportunities for the advancement of research, such as Machine Learning (ML) and Data Mining (DM). However, there are some limitations that may reduce the accuracy of some traditional algorithms of these areas, for example the imbalance of classes samples in a dataset. To mitigate this drawback, some solutions have been the target of research in recent years, such as the development of techniques for artificial balancing data, algorithm modification and new approaches for imbalanced data. An area little explored in the data imbalance vision are the problems of hierarchical classification, in which the classes are organized into hierarchies, commonly in the form of tree or DAG (Direct Acyclic Graph). The goal of this work aims at investigating the limitations and approaches to minimize the effects of imbalanced data with hierarchical classification problems. The experimental results show the need to take into account the features of hierarchical classes when deciding the application of techniques for imbalanced data in hierarchical classification. (AU) | |
| FAPESP's process: | 13/15856-8 - Techniques for unbalanced data in hierarchical classification |
| Grantee: | Victor Hugo Barella |
| Support Opportunities: | Scholarships in Brazil - Master |