Banca de QUALIFICAÇÃO: CEPHAS ALVES DA SILVEIRA BARRETO

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : CEPHAS ALVES DA SILVEIRA BARRETO
DATE: 10/12/2021
TIME: 09:00
LOCAL: remoto
TITLE:

Selection and Labelling Instances for wrapper-based semi-supervised methods

KEY WORDS:

Machine Learning; Semi-supervised Learning; Wrapper Methods; Selection and
Labelling of Instances.

PAGES: 110
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Sistemas de Informação
SUMMARY:

In recent years, the use of Machine Learning (ML) techniques to solve real problems has
become very common and a technological pattern adopted in plenty of domains. However,
several of these domains do not have enough labelled data to give ML methods a good
performance. This problem led to the development of Semi-supervised methods, a type of
method capable of using labelled and unlabelled instances in its model building. Among
the semi-supervised learning methods, the wrapper methods stand out. This category of
methods uses a process, often iterative, that involves: training the method with labelled
data; selection of the best data from the unlabelled set; and labelling the selected data.
Despite showing a simple and efficient process, errors in the selection or labelling processes
are common, which deteriorate the final performance of the method. This research aims
to reduce selection and labelling errors in wrapper methods to establish selection and
labelling approaches that are more robust and less susceptible to errors. To this end, this
work proposes a selection and labelling approach based on classification agreement and a
selection approach based on distance metric as an additional factor to an already used
selection criterion (e.g. confidence or agreement). The proposed approaches can be applied
to any wrapper method and were tested on 42 datasets in Self-training and Co-training
methods. The results obtained so far indicate that the proposals bring gains for both
methods in terms of accuracy and F-measure.

BANKING MEMBERS:
Presidente - 1350250 - ANNE MAGALY DE PAULA CANUTO
Externo ao Programa - 4351681 - JOAO CARLOS XAVIER JUNIOR
Externa ao Programa - 2720574 - KARLIANE MEDEIROS OVIDIO VALE
Externo à Instituição - DIEGO SILVEIRA COSTA NASCIMENTO - IFRN