Banca de QUALIFICAÇÃO: JESAÍAS CARVALHO PEREIRA SILVA

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : JESAÍAS CARVALHO PEREIRA SILVA
DATE: 14/06/2024
TIME: 14:30
LOCAL: Remoto
TITLE:

An investigation of dinamicity in the parameter selection of the classifier ensembles

KEY WORDS:

Classifier ensembles. Dynamic structure selection. Combination methods. Region of Competence.

PAGES: 55
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Sistemas de Informação
SUMMARY:

Over the years, significant progress has been made in the realm of classifier ensembles
research. Several methods to enhance their efficiency have been proposed, applicable to
both homogeneous and heterogeneous ensemble structures. A key challenge in employing
classifier ensembles lies in determining their structure (hyper-parameters). Basically, the
ensemble structure selection can be done in two different ways, static and dynamic selection.
Unlike static selection, which regardless of the parameters uses the same criteria to perform
the classification, dynamic selection defines the ensemble structure for each test instance.
Different dynamic selection methods have been proposed in the literature, mainly for
ensemble members and dataset features, but little effort has been made to propose dynamic
selection methods for combination methods, also known are fusion methods. Therefore, it
is important to evaluate the impact of dynamic selection of combination methods or both
(methods and members) in creating robust ensembles. This work proposes an exploratory
analysis of the dynamic selection of the main parameters of an ensemble structure. To this
end, three different scenarios will be evaluated: Full static ensembles; Partially dynamic
ensembles; and, Full dynamic ensembles. In order to analyze the dynamic scenarios, three
dynamic fusion methods are proposed. Each one focuses on a specific approach: one by
region of competence, another by meta-learning, and the last by fuzzy hyperbox. Finally, an
empirical analysis of these scenarios will be carried out. The partial results show that the
use of dynamic selection both in the ensemble members and in the combination methods
using the dynamic fusion by region of competence form more robust ensembles. Thus, it is
expected that other dynamic fusion techniques can also improve and boost the results of
the classifiers used.

COMMITTEE MEMBERS:
Presidente - 1350250 - ANNE MAGALY DE PAULA CANUTO
Externo à Instituição - ARAKEN DE MEDEIROS SANTOS - UFERSA
Externo à Instituição - GEORGE DARMITON DA CUNHA CAVALCANTI - UFPE
Externa à Instituição - HULIANE MEDEIROS DA SILVA